Image analysis for determining characteristics of humans

ABSTRACT

Systems and methods are disclosed for predicting one or more characteristics of a animal by applying computational methods to image(s) of the animal to generate one or more metrics indicative of the characteristics. Embodiments determine predictors of characteristics by creating a sample library of animals of a particular type, determining facial descriptor measurements for each animal, determining relationships between facial descriptor measurements and additional library data, and selecting predictors from these relationships. Other embodiments predict characteristics of animals not in the library and, optionally, categorize animals for particular discipline, training, management, care, etc. based on the characteristics. Other embodiments predict characteristics and determine strategies for group(s) of animals using predicted characteristics of individual animals. Embodiments are broadly applicable to domesticated animals including dogs, cats, cattle, oxen, llamas, sheep, goats, camels, geese, horses, chickens, turkeys, and pigs. Other embodiments predict certain characteristics of humans, including certain cognitive or developmental disorders.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of U.S. Provisional ApplicationSer. No. 61/484,126, entitled “Image Analysis for DeterminingCharacteristics of Animals,” which was filed May 9, 2011 and isincorporated herein by reference. This application also claims thepriority of U.S. Provisional Application Ser. No. 61/616,234, entitled“Image Analysis for Determining Characteristics of Animals,” which wasfiled Mar. 27, 2012 and is incorporated herein by reference.

TECHNICAL FIELD

The disclosure herein relates to the objective determination of acharacteristic of an animal or human by applying computational methodsto one or more images of the animal or human to generate one or moremetrics indicative of the characteristic of interest. It also relates tothe pairing of animals and humans that are better suited to worktogether.

BACKGROUND

Animal domestication can be thought of as developing a mutually usefulrelationship between animals and humans. Over the past 12,000 years,humans have learned to control their access to food and othernecessities of life by changing the behaviors and natures of wildanimals. All of today's domesticated animals—including dogs, cats,cattle, oxen, llamas, sheep, goats, camels, geese, horses, chickens,turkeys, and pigs—started out as wild animals but were changed over thecenturies and millennia into animals that are tamer, quieter, andgenerally more cognitively suited to a lifestyle of coexistence withhumans. Today people benefit from domesticated animal in many waysincluding keeping cattle in pens for access to milk and meat and forpulling plows, training dogs to be guardians and companions, teachinghorses to adapt to the plow or take a rider, and changing the lean,nasty wild boar into the fat, friendly pig.

When individuals are looking to breed animals, they look for certaintraits in purebred stock that are valued for a particular purpose, ormay intend to use some type of crossbreeding to produce a new type ofstock with different, and, it is presumed, superior abilities in a givenarea of endeavor. For example, to breed chickens, a typical breederintends to receive eggs, meat, and new, young birds for furtherreproduction. Thus, the breeder has to study different breeds and typesof chickens and analyze what can be expected from a certain set ofcharacteristics before he or she starts breeding them. On the otherhand, purebred breeding aims to establish and maintain stable traitsthat animals will pass to the next generation. By “breeding the best tothe best,” employing a certain degree of inbreeding, considerableculling, and selection for “superior” qualities, one could develop abloodline superior in certain respects to the original base stock.

As first noted by Charles. Darwin, domesticated animals are known toshare a common set of physical characteristics, sometimes referred to asthe domestication phenotype. C. Darwin, THE VARIATION OF ANIMALS ANDPLANTS UNDER DOMESTICATION (2^(nd) ed.) (New York: D. Appleton & Co.,1883). They are often smaller, with floppier ears and curlier tails thantheir untamed ancestors. Their coats are sometimes spotted while theirwild ancestors' coats are solid. One long-term study demonstrating thisphenomenon has been ongoing since 1958 at the Institute of Cytology andGenetics in Novosibirsk, Russia. In this study, scientists havesuccessfully demonstrated that, through careful selective breeding fortamability, wild Siberian silver foxes acquire both the behavioral andappearance traits of domesticated dogs. See, e.g., L. Trut, Early CanidDomestication: The Fox Farm Experiment, 87 AMERICAN SCIENTIST 160-69(March-April 1999). This highly conserved combination of psychologicaland morphological changes during the process of domestication is seen tovarying degrees across a remarkably wide range of species, from horses,dogs, pigs, and cows to some non-mammals like chickens and even a fewfish. However, in no other species has this relationship betweenbehavior and anatomical features been more widely noted than in thehorse.

The partnership between human and horse is among the earliest bondsformed between mankind and the animal world. Archeological findingsestimate that horses have been domesticated for approximately 5,500years, and throughout this extended relationship these two cohabitatingspecies have certainly left a mark on one another. Few majorcivilizations exist in pre-modern history, that did not make use of thehorse's strength and speed for survival and economic prosperity. As aresult of this dependence, centuries of selective breeding have seenmankind gradually reshape the horse from the form of its wild forbearersinto the athletic and reliable working partner that we know today. Thevalue that early breeders placed on physical attributes such as size,color, and build varied greatly by region largely as a product ofdiffering climates, terrains, and lifestyles. However, all earlyhorsemen placed special emphasis on breeding for horses cognitivelycapable of thriving in a human environment and working relationship. Itwas from this early focus on behavioral characteristics in thedevelopment, of the domesticated horse that the practice of relatingphysiological aspects of the equine face to aspects personality wasfirst derived. From the earliest mentions in the ancient Bedouinbreeding books of 300 B.C., to the extensive facial analysis techniquesof the Gypsy tribes of eighteenth-century Russia, nearly every majorequestrian culture in history has recognized a relationship betweenphysiological features of the equine face and innate traits ofpersonality. Even amongst the many technological and scientificadvancements of the modern era, today's multi-billion dollar horseindustry has still held fast to many of its long-standing traditions andcustoms, including the use of facial analysis techniques to predictequine personality and trainability.

Relationships also exist in humans between physiological feature sets(i.e., phenotypes) and certain cognitive functions and/or personalitytraits. During progressive stages of human embryonic growth, developmentof the brain and face remains intimately connected through both geneticsignaling and biomechanical/biochemical mechanisms. The face developsfrom populations of cells originating from the early neural crest, withcells from the neural tube gradually shifting to form the prominences ofthe face. Differentiation of these early cells is closely regulatedthrough intricate genetic signaling mechanisms, with the brainessentially serving as the platform on which the face grows. As thesetwo structures continue to grow and develop during the later embryonicstages, their phenotypes remain closely linked as complex genetichierarchies regulate patterns of cross talk between molecules, cells,and tissues.

SUMMARY

Embodiments comprise a method for measuring an animal to determine oneor more characteristics of the animal, comprising receiving one or moredigital images representing said animal, storing the images in acomputer memory, adding a plurality of reference points to the storeddigital images, and computing one or more metrics relating to thecharacteristic of the animal using the reference points. Otherembodiments comprise a method for determining a characteristic of ananimal based on a set of metrics related to the animal, comprisingselecting one or more metrics from the set of metrics, calculating acombined metric using the selected metrics, and determining thecharacteristic of the animal based on the value of the combined metric.Other embodiments comprise computer systems that implement one or moreof the above methods.

Other embodiments comprise methods and systems that can be used topredict certain characteristics of humans, including certain humancognitive or developmental disorders. The methods include measurementsof facial features and the use of such measurements in computations.Other embodiments pair humans and animals based on characteristicsand/or various types of suitability, for example suitability to work orperform a certain task together.

Other embodiments include a method for predicting a characteristic of atype of animal comprising, for each of a plurality of individual animalsof the type, storing one or more digital images representing theindividual animal in a memory operably connected to a digital computer;annotating the one or more digital images with a plurality of referencepoints; associating at least one other data value about the individualanimal with the one or more digital images representing the individualanimal; computing, with the digital computer, one or more metrics usingthe plurality of reference points. The method further comprisesselecting a combination of the one or more metrics for predicting thecharacteristic of the animal. In some embodiments, the selecting stepfurther comprises determining one or more relationships between the oneor more metrics and the at least one other data value for the pluralityof individual animals and the combination is selected based on the oneor more relationships. Other embodiments comprise systems andcomputer-readable media embodying these methods.

Other embodiments include a method for determining a characteristic ofan animal based on one or more metrics related to the animal, comprisingstoring one or more digital images representing the animal in a memoryoperably connected to a digital computer; annotating the one or moredigital images with a plurality of reference points; computing, with thedigital computer, the one or more metrics using the plurality ofreference points; and predicting the characteristic of the animal basedon the one or more metrics. In some embodiments, the predicting stepfurther comprises computing a combined metric based on a predeterminedfunction of the one or more metrics and predicting the characteristicbased on the combined metric. Other embodiments comprise systems andcomputer-readable media embodying these methods.

Other embodiments include a method for predicting one or morecharacteristics of a group of animals of the same type comprising, foreach of the individual animals within the group, storing one or moredigital images representing the individual animal in a memory operablyconnected to a digital computer; annotating the one or more digitalimages with a plurality of reference points; computing, with the digitalcomputer, one or more metrics using the plurality of reference points;and predicting one or more characteristics of the individual animalbased on the one or more metrics. The method further comprisespredicting the one or more characteristics of the group of animals basedon the predicted one or more characteristics of the individual animalscomprising the group. Some embodiments further comprise determining astrategy for maintaining or managing the group of animals based on thepredicted one or more characteristics of the group of animals. Someembodiments further comprise computing one or more combined metrics,each based on a predetermined function of the one or more metrics, andpredicting the one or more characteristics of the individual animalbased on the one or more combined metrics. Other embodiments comprisesystems and computer-readable media embodying these methods.

Other embodiments include a method for determining a characteristic of ahuman or animal subject, comprising calculating two or more ratios basedupon metrics related to a subject's head, wherein distances or anglesbetween reference points on the subject's head are used; predicting,using a computer and computations, a characteristic of the subjectwherein the two or more ratios are used and wherein data about a groupof subjects are referenced; and providing the predicted characteristicto an output device. Other embodiments comprise systems andcomputer-readable media embodying these methods.

Other embodiments include a method for determining a characteristic of aperson based on one or more metrics related to the person, comprisingstoring one or more digital images representing the person in a memoryoperably connected to a digital computer; annotating the one or moredigital images with a plurality of reference points; computing, with thedigital computer, the one or more metrics using the plurality ofreference points; and predicting the characteristic of the person basedon the one or more metrics. Other embodiments comprise systems andcomputer-readable media embodying these methods.

Other embodiments include a method for choosing a combination of aperson and an animal for a particular task, comprising computing one ormore metrics related to the person; computing one or more metricsrelated to each of a plurality of animals; computing a combinationcharacteristic related to the combination of the person with each of theplurality of animals, based on at least a portion of the one or moremetrics related to the person and at least a portion of the one or moremetrics related to each of the plurality of animals; and determining thecombination of the person and one of the plurality of animals based onthe computed combination characteristics. Other embodiments comprisesystems and computer-readable media embodying these methods.

Other embodiments include a method for choosing a combination of aperson and an animal for a particular task, comprising computing one ormore metrics related to the animal; computing one or more metricsrelated to each of a plurality of persons; computing a combinationcharacteristic related to the combination of the animal with each of theplurality of persons, based on at least a portion of the one or moremetrics related to the animal and at least a portion of the one or moremetrics related to each of the plurality of persons; and determining thecombination of the animal and one of the plurality of persons based onthe computed combination characteristics. Other embodiments comprisesystems and computer-readable media embodying these methods.

DESCRIPTION OF THE DRAWINGS

The detailed description will refer to the following drawings, whereinlike numerals refer to like elements, and wherein:

FIG. 1A shows facial description measurement AR01_Degree of FacialInflexion;

FIG. 1B shows facial description measurement AR01_Degree of NoseRounding;

FIG. 1C shows facial description measurement AR01_Face ThicknessProportion;

FIG. 1D shows facial description measurement AR01_Forehead SlopeProportion;

FIG. 1E shows facial description measurement AR01_Forehead HeightProportion;

FIG. 1F shows facial description measurement AR01_Forehead LengthProportion;

FIG. 1G shows facial description measurement AR01_Nose LengthProportion;

FIG. 1H shows facial description measurement AR01_Nose RoundnessProportion;

FIG. 1J shows facial description measurement AR01_Nostril PositionProportion;

FIG. 1K shows facial description measurement AR01_Degree of Eye OrbitalProtrusion;

FIG. 2 shows facial description measurement AR02_Degree of FacialProtuberance;

FIG. 3A shows facial description measurement AR03_Jowl ProtuberanceProportion;

FIG. 3B shows facial description measurement AR03_Jowl RoundnessProportion;

FIG. 3C shows facial description measurement AR03_Jowl-to-UnderlineProportion;

FIG. 4A shows facial description measurement AR04_Forehead Height Angle

FIG. 4B shows facial description measurement AR04_Full Angle Face;

FIG. 4C shows facial description measurement AR04_Mouth Inflexion Angle;

FIG. 4D shows facial description measurement AR04_Muzzle RoundnessProportion;

FIG. 4E shows facial description measurement AR04_Muzzle SizeProportion;

FIG. 4F shows facial description measurement AR04_Muzzle Slope Angle;

FIG. 5A shows facial description measurement AR05_Chin FirmnessProportion;

FIG. 5B shows facial description measurement AR05_Chin FullnessProportion;

FIG. 5C shows facial description measurement AR05_Chin Length Angle;

FIG. 5D shows facial description measurement AR05_Chin Thickness Angle;

FIG. 5E shows facial description measurement AR05_Chin Width-to-HeightProportion;

FIG. 5F shows facial description measurement AR05_Lip Length Proportion;

FIG. 6A shows facial description measurement AR06_Lip ProtuberanceProportion;

FIG. 6B shows facial description measurement AR06_Mouth LengthProportion;

FIG. 7A shows facial description measurement AR07_Degree of NostrilFlutedness;

FIG. 7B shows facial description measurement AR07_Degree of NostrilRoundness;

FIG. 7C shows facial description measurement AR07_Inner NostrilConvergence Proportion;

FIG. 7D shows facial description measurement AR07_Nose Width-to-HeightProportion;

FIG. 7E shows facial description measurement AR07_Nostril LengthProportion;

FIG. 7F shows facial description measurement AR07_Nostril WidthProportion;

FIG. 8 shows facial description measurement AR08_Degree of LipInflexion;

FIG. 9A shows facial description measurement AR09_Degree of Ear Flare;

FIG. 9B shows facial description measurement AR09_Ear InflexionProportion;

FIG. 9C shows facial description measurement AR09_Ear RoundnessProportion;

FIG. 9D shows facial description measurement AR09_Ear Width-to-BreadthProportion;

FIG. 10A shows facial description measurement AR10_Ear RotationProportion;

FIG. 10B shows facial description measurement AR10_Ear Set Angle;

FIG. 11A shows facial description measurement AR11_Eye HeightProportion;

FIG. 11B shows facial description measurement AR11_Eye Extrema IntersectAngle;

FIG. 11C shows facial description measurement AR11_Eye Height-to-LengthProportion;

FIG. 11D shows facial description measurement AR11_Eye HeightProportion;

FIG. 11E shows facial description measurement AR11_Eye Orbital LateralProtuberance Proportion;

FIG. 11F shows facial description measurement AR11_Eye ProtuberanceProportion;

FIG. 11G shows facial description measurement AR11_Eye RoundnessProportion;

FIG. 11H shows facial description measurement AR11_Eye Size Proportion;

FIG. 11J shows facial description measurement AR11_Eye SizeProportion_(Length);

FIG. 11K shows facial description measurement AR11_Lower Minima PointProportion Eye;

FIG. 11L shows facial description measurement AR11_Top Eye Angle;

FIG. 11M shows facial description measurement AR11_Upper Maxima PointProportion Eye;

FIG. 12 shows facial description measurement AR12_Forehead Width Angle;

FIG. 13A shows facial description measurement AR13_Cheek-to-ZygomaticHeight Ratio;

FIG. 13B shows facial description measurement AR13_Zygomatic RidgeAngles;

FIG. 14 shows various relationships between genes, hormones, behavior,and facial features of an animal;

FIG. 15 shows a method for determining a predictor of a characteristicof an animal according to an embodiment of the present disclosure;

FIG. 16 shows a method for determining a characteristic of an animalaccording to another embodiment of the present disclosure;

FIG. 17 shows a method for determining a maintenance or managementstrategy for one or more animals according to another embodiment of thepresent disclosure;

FIG. 18 shows an exemplary application of an embodiment of the presentdisclosure in categorizing horses into one of three different equestrianevents;

FIG. 19 shows an exemplary application of an embodiment of the presentdisclosure in predicting the performance of horses in a particularequestrian event;

FIG. 20 shows an exemplary hierarchical system of the features of theequine facial profile;

FIG. 21 shows an exemplary receiver operating characteristic (ROC)curve;

FIG. 22 shows a system that can be used to implement any of the methodsof the present disclosure;

FIG. 23A shows facial descriptor measurement Eye Extreme IntersectAngle, θ;

FIG. 23B shows facial descriptor measurement Eye Depth Proportion, A/B;

FIG. 23C shows facial descriptor measurement Eye Height Proportion, A/B;

FIG. 23D shows facial descriptor measurement Eye Length-to-HeightProportion, (B+C)/A;

FIG. 23E shows facial descriptor measurement Lower Eye Angle, θ;

FIG. 23F shows facial descriptor measurement Upper Eye Angle, θ;

FIG. 24 shows facial descriptor measurement Mid-Face Width Proportion,(A+B)/C,

FIG. 25 shows facial descriptor measurement Upper Eyelid Proportion,A/(A+B);

FIG. 26 shows facial descriptor measurement Palpebral Angle, θ;

FIG. 27 shows facial descriptor measurement Eye Roundness Proportion,A/B;

FIG. 28 shows facial descriptor measurement Philtrum DefinitionProportion, A/B;

FIG. 29A shows facial descriptor measurement Upper Lip ThicknessProportion, A/B;

FIG. 29B shows facial descriptor measurement Lower Lip ThicknessProportion, A/B;

FIG. 30 shows facial descriptor measurement Philtrum Length Proportion,A/B; and

FIG. 31 shows a method for determining which animal among a plurality ofanimals is the best match for a particular human engaged in a particulartask, according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Various methods for using metrics associated with the physical form ofan animal or human (including facial measurements) to predict acharacteristic including behavior or suitability are described. While agreater number of examples are shown or described with respect tohorses, pigs and humans, the methods apply equally to other mammals andnon-mammals. Also, the methods described are used for the grouping,pairing or matching of mammals and non-mammals.

Methods for analyzing equine facial characteristics vary across thehorse industry, and most are cherished as highly guarded trade secretstypically only passed down from aging trainers to their selectedsuccessors. To date there have been no formal scientific studies carriedout on this topic, and only one published text exists which delineatesone of these behavioral evaluation methods, namely “Getting in TTouch:Understand and Influence Your Horse's Personality” by LindaTellington-Jones (North Pomfret: Trafalgar Square Publishing, 1995). Asa result, this valuable but purely non-quantitative method remainsobscure, highly subjective, and inaccessible to many individuals in theequestrian industry.

Within the equestrian industry there are many different styles of ridingrecognized as distinct riding disciplines. The riding discipline that ahorse is trained for is the earliest and arguably the most importanttraining decision of a horse's competitive career. Disciplines differwidely in the conformational characteristics that they favor and thephysical demands that they place on the equine athlete. However, just asin human athletes, horses must also be cognitively well suited to thevarying mentalities required within differing equestrian sports tosucceed at the top levels of competition.

Due to the relationship in horses between facial features andtemperament, in the experiments patterns of facial features werediscerned between horses that excel in cognitive distinct ridingdisciplines. The embodiments include an effective computer model of theequine face quantitatively reflecting this pattern, allowing the ridingdiscipline of a horse to be predicted computationally with a high degreeof accuracy. Similarly, models derived from computationally determinedfacial measurements can be used to predict a horse's performance (e.g.,win percentile) within a given riding discipline based on its relativecognitive suitability to that training style.

Moreover, the need for a computational system for analyzing facialcharacteristics to determine behavior and performance traits affected bythe innate nature of an animal extends well beyond equine applications.For example, in addition to being companions, humans use dogs in manyspecialized ways such as police dogs, bomb-sniffing dogs,seeing-eye/service dogs, herding dogs, hunting dogs, cadaver dogs,therapeutic dogs, etc. Each of these specialized uses requiressignificant training. Like horses, dogs must be cognitively suited tothe rigors of a particular specialization and it is imperative that thisbe determined as soon as possible due to the significant investmentrequired for training. Dogs with behavioral traits making them suitablefor particular specializations should have corresponding discerniblefacial features, much like the Russian foxes discussed above. Aneffective model of the canine face quantitatively reflects this pattern,enabling a highly-accurate, computational prediction of the suitabilityof a dog for a particular specialization.

Similar needs also exist with respect to animals raised to produce foodor other materials for human consumption. For example, there isextensive debate in the swine production industry over the use ofgestation crates versus gestation pens. Gestation crates are individualenclosures that allow the animal only enough room to stand up and liedown. In intensive swine production systems, such crates are thetraditional housing option for sows during gestation. Many farmerscontend that these crates enable them to provide sows withindividualized care. Nevertheless, concerns from pork consumers over themental and emotional welfare of sows living in these types of enclosureshave placed considerable pressure on the swine production industry toadopt group housing such as gestation pens. The European Union hasmandated that all gestation crates must be phased out of swineproduction systems by 2015. Although there is no correspondingregulation in the U. S., recently U. S. swine producers have facedsignificant market pressure from large buyers to begin phasing outgestation crates in favor of gestations pens. One of the industry'slargest customers—a well-known fast-food chain—recently called for itssuppliers to shift toward gestation pens, and other large customers arelikely to enact similar requirements. The ultimate source of this U. S.market pressure is consumer concerns about swine welfare.

While the switch from crates to pens will provide sows with significantphysical and emotional benefits, it is not without costs and risks. As aspecies, pigs are extremely aggressive animals that fight to establishand maintain social hierarchies. As farmers increasingly house theirsows as groups in gestation pens, they will encounter new problemsrelated to fights among these naturally aggressive animals. These fightscan cause serious injury—including miscarriage—or even death to the sowsinvolved, which is extremely undesirable to swine farmers.

To avoid such outcomes, what is needed is a method that reliablypredicts aggression-related personality characteristics of individualsows and to what extent certain individuals will cohabitate in agestation pens without harming each other. As discussed above withreference to horses, a solution to this problem is to use swine facialfeatures to predict aspects of swine personality that correlate withaggression. Once these are known, an individual's aggression-relatedpersonality traits can be used to predict the outcome of that individualinteracting socially with another individual. These predicted pair-wiseoutcomes can be used to predict the level or incidence of aggressionamong a larger group cohabitating in a gestation pen. By doing so, onecan allocate groups of sows to individual gestation pens based onminimizing risk of aggression-related injuries to the individuals.

A reliable method to predict aggression-related personalitycharacteristics of individual sows also would allow hog farmers to breedless aggressive animals through their selection of replacement gilts,thereby reversing the trend of increasingly aggressive and unmanageablehogs created through excessive breeding for weight gain. Such a methodalso may allow farmers to implement intelligent and humane animalmanagement strategies. For example, sows predicted to be excessivelyaggressive can be placed in gestation crates while the rest of thepopulation can be placed in more open gestation pens.

Similarly, there is a need to reduce the incidence of “mash deaths”among pig litters. Such deaths are among the leading cause of litterlosses in lactation-age piglets (0-3 weeks). They occur when a piglet isunable to move out of the way of its mother as she lies down, andbecomes asphyxiated under her greater body weight. Evidence suggeststhat mash deaths are much more common in pigs raised in large-scaleproduction systems compared to farm-raised pigs, due in part to theconfined living conditions and restricted fields of vision thatfarrowing crates place on sows as they nurse their litters.

Nevertheless, a sow's insufficient reactivity also contributes to thegreater incidence of mash deaths in large-scale production systems. Aswild animals, sows instinctively rose to their feet quickly wheneverthey heard distress cries or felt a piglet struggling beneath them.Domesticated sows appear to have lost this protective mothering reflex.Many experts believe that this loss of mothering instinct is part of thedegradation of behavioral traits among high-performance pig lines due toheavy over-breeding for traits such as lean weight gain and weaningweights. If mash deaths are due to a loss of genetic potential formothering, then the associated endocrine changes will also produceobservable and quantifiable changes to facial morphology.

In such case, the anatomical structures of a sow's face are used todevelop a computational model for predicting the expected number of mashdeaths that the sow would incur per litter. The predicted behavioraloutcomes are used to determine various management and/or maintenancestrategies for high-risk sows, including increased supervision, largerpens, moving piglets off to reduce litter size, etc. Farmers could alsouse this model to select young female pigs (“gilts”) as replacements fortheir maintenance rotation by taking into consideration which ones wouldmake the most productive mothers. This would allow them to avoid themonetary loss of feeding a gilt to farrowing age only to have her proveunsuitable as a mother. It would also allow farmers to improve themothering ability of their herd over time by selective breeding.

There exists a similar need to identify the existence ofmothering-related behavioral traits in sheep. Many of the births by ewesare twins. However, certain ewes will not accept the second-born twinunder any circumstances. In such cases, the anatomical structures of anewe's face is used to develop a computational model to predict thatindividual's behavioral traits associated with mothering capacity,including willingness to accept all offspring. The predicted behavioraltraits are used to determine various management solutions and/ormaintenance strategies including identifying ewes with higher rejectionrate potentials. These animals would benefit from being brought intoconfined birthing pens prior to parturition to allow for a moreefficient imprinting process than would occur in open range-managementsituations. These computational models also could be used to predictewes with highest capacities for accepting lambs; these ewes would thenbecome candidates for receiving grafted lambs who could not be imprintedonto their original mother. These results benefit both animals andfarmers.

Similar needs exist for identifying traits such as behavioral traits ofanimals used for purposes of recreation, training, work, etc. Forexample, there has been much concern about the welfare of horses andcattle used in rodeos, in particular the ones that buck aggressivelywhile attempting to throw off a rider. While many of these animals arenaturally aggressive, a certain amount of this behavior is due to theuse of devices such as straps, electric prods, etc. that increaseaggression but that many consider to be inhumane. Accordingly, it wouldbe beneficial to use a computational model based on the facialstructures of a horse or bull to measure or estimate a prospective rodeoanimal's natural level of aggressive behavior. By selecting only theanimals whose innate characteristics make them most suitable, rodeoscould avoid the need for these controversial devices.

Consequently, one of the objects of the disclosure is to revolutionize,with the use of modern imaging and computing technology, the processesof ancient equine behavioral evaluation systems. Another object of thedisclosure is to bring objectivity to an animal facial analysis processby utilizing mathematical techniques to quantify the relevantphysiological facial features of animals such as horses, donkeys,cattle, oxen, llamas, sheep, goats, dogs, camels, geese, chickens,turkeys, cats, ferrets, and pigs. Another object is establish a set ofmeasurements that provides a quantitative model for a type of animalthat is effective for determining a characteristic of a particularanimal of that type, such as the most suitable riding discipline orexpected performance within a particular riding discipline for a horse.Another object is to provide a computational method for determining andevaluating a characteristic of a particular horse based on the model ofthe equine face. Yet another object is to provide a user-friendly systemthat embodies this computational method and is based onreadily-available digital computing and imaging technology. Moreover,another object is to improve the efficacy of animal management andtraining by providing a computationally-efficient, accurate, andobjective technique for predicting an animal's innate personalitycharacteristics that affect the results of activities in which theanimal may participate, such as a race or event.

The biological mechanism relied upon in the disclosed embodiments wasfirst proposed in the ground-breaking 1999 Russian study entitled “EarlyCanid Domestication: The Fox Farm Experiment.” Using an extensivebreeding program of wild silver foxes, this study showed that selectivebreeding can be used to alter the innate personality traits orcharacteristics of a line of domesticated animals. More particularly,this study demonstrated that endocrine or hormone changes cause both thepersonality trait changes and a predictable suite of morphologicalchanges, most predominantly in the structures of the face.

As illustrated graphically in FIG. 14, the innate personality of ananimal originates in its genetic composition. Genes dictate the basallevels of neurologically active hormones that control the behavior of ananimal, such serotonin which inhibits aggression. Genes also dictate thetiming of the release of these hormones, such as corticosteroids (stressresponse) that control the windows of postnatal cognitive and socialdevelopment in young animals. The cognitive framework of an animal isdetermined from a combination of these innate personality traitsprovided by this genetically controlled endocrine makeup—the so-called“nature effect”—and the stimuli and experiences that the animal wassubject to during development—the so-called “nurture effect.” Whenviewed in the context of the animal's current environment, thiscognitive framework dictates its behavioral performance, which may bedefined in such terms as cognitive suitability to a specific task,success in performing a specific task, likelihood of displaying aspecific type or pattern of behavioral responses, or, when comparedagainst the personality types of its conspecifics, performance of anindividual in group situations.

As mentioned above, variation in the basal levels of neurologicallyactive hormones and their release windows during development account notonly for differences in innate personality among animals of the samespecies, but also for variability in morphology, particularly of theface. This direct correlation between the facial structure and endocrinecomposition of an animal subsequently allows for quantifiable featuresof an animal's face to be correlated with and used as a proxy forpredicting variability in the innate behavior of individual animals as afunction of their neurochemical makeup. Variations in facial structuremay also be used to predict the behavior and performance of an animal asa result of the variations in the degree of functionality that theyallow, in terms such as field of vision, auditory acquisition, oxygenintake, feed intake, etc.

Various facial recognition and image matching techniques willmathematically model the equine face and allow the prediction ofbehavior and performance. While these embodiments are effective, theprocesses and techniques of facial recognition and image matching aregenerally computationally intensive. Therefore, trigonometric modelingis used by some embodiments. Combinations of facial/shape recognition,image matching and trigonometric modeling may be used to predictbehavior and performance.

For example, equine facial features may be quantified based on thirteenindependent anatomical regions. Within each region an individual horsecan possess one of a plurality of discrete facial classification. Thedistinctions between these classifications, however, vary by anatomicalregion. Classifications in less structurally diverse regions can bedescribed by a single structural feature, which can in turn be modeledwith a single continuous measure. Classifications in anatomical regionswith high structural complexity, however, show hierarchicalrelationships dependent on multiple structural features that must bemodeled with multiple measured variables to distinguish betweenclassifications. The first step in developing a mathematical model ofthe equine face was to identify the anatomical regions that showhierarchical relationships and determine the individual structuralfeatures that distinguish between classifications in these more complexsystems. FIG. 17 shows an exemplary hierarchical system of equine facialprofile features.

Based upon the identified continuous traits and hierarchical system, theidentified structural features of relevance can be described usinglength measure and angles. Accordingly, a trigonometric model can beused to accurately characterize the equine face in lieu of morecomputationally expensive facial recognition and image matchingtechniques. FIGS. 1 through 13 illustrate the trigonometric model foreach of the thirteen independent anatomical regions, denoted AR01through AR13, respectively. Certain anatomical regions comprise multiplefacial descriptor measurements. For example, AR01 (Facial Profile)comprises ten different facial descriptor measurements, denotedAR01_Degree of Facial Inflexion, AR01_Degree of Nose Rounding, AR01_(—)Face Thickness Proportion, AR01_Forehead Slope Proportion, AR01_(—)Forehead Height Proportion, AR01_(—) Forehead Length Proportion,AR01_Nose Length Proportion, AR01_Nose Roundness Proportion,AR01_Nostril Position Proportion, and AR01_Degree of Eye OrbitalProtrusion, as illustrated in FIGS. 1A through 1K, respectively. Intotal, the thirteen anatomical regions AR01 through AR13 comprisefifty-six (56) facial descriptor measurements as illustrated in FIGS. 1through 13. The corresponding mathematical descriptions for each ofthese measurements are provided below.

Descriptor Name: Degree of Nose Rounding Anatomical Region of Face:AR01_FacialProfile Describes the degree to which the boney structures ofthe nose rounds away from the true line of the face. Partiallydifferentiates between “Roman” and “Moose” noses.$M_{ac} = \frac{\left( {A_{y} - C_{y}} \right)}{\left( {A_{x} - C_{x}} \right)}$finds the slope of line AC N_(x) = B_(x) − (A_(x) − B_(x)) finds thex-coordinate for “imaginary” point N sufficiently far to the left ofpoint B to complete the triangle N_(y) = M_(ac) * (N_(x) − C_(x)) +C_(y) finds the y-coordinate of imaginary point N AB = {square root over((B_(x) − A_(x))² + (B_(y) − A_(y))²)}, BN = {square root over((B_(x) − N_(x))² + (B_(y) − N_(y))²)}, CN = {square root over((C_(x) − N_(x))² + (C_(y) − N_(y))²)}, BC = {square root over((B_(x) − C_(x))² + (B_(y) − C_(y))²)} finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{BN}}^{2} - {\overset{\_}{BC}}^{2} - {\overset{\_}{CN}}^{2}}{{- 2}*\overset{\_}{BC}*\overset{\_}{CN}} \right)}$finds the angle for ∠BCN, denoted here as θ BL = sin(θ) * BC finds theheight of triangle ΔCBN (perpendicular displacement of Upper Nose Pointfrom Upper Profile of Face) M_(bl) = −(M_(ac))⁻¹ finds the slope of theslope perpendicular to the line of the upper face$L_{x} = \frac{\left( {{M_{bl}*B_{x}} - {M_{ac}*A_{x}} + A_{y} - B_{y}} \right)}{\left( {M_{bl} - M_{ac}} \right)}$finds the x-coordinates of the intersection point between the line ofthe upper face and its perpendicular bisector inclusive of point B$\sigma_{u} = \frac{\left( {L_{x} - B_{x}} \right)}{\left( \left| {L_{x} - B_{x}} \right| \right)}$finds the constant of nostril position with magnitude 1 indicatingdirection of position relative to the upper line of the face via sign(±) ${DoNR} = {\sigma_{u}*\left( \frac{d}{\overset{\_}{AB}} \right)}$finds the Degree of Nostril Rounding, here defined as the degree towhich the Upper Nostril Point rounds away from the True Line of the Facerelative to the overall length of the face Descriptor Name: ForeheadLength Proportion Anatomical Region of Face: AR01_FacialProfileDescribes the length of the horse's forehead relative to the overalllength of the head.$M_{ubb} = \frac{\left( {U_{y} - {bb}_{y}} \right)}{\left( {U_{x} - {bb}_{x}} \right)}$finds the slope of the True Line of the Head M_(p) = −(M_(ubb))⁻¹ findsthe slope perpendicular to the True Line of the Head${R_{x} = \frac{\left( {{M_{ubb}*U_{x}} - {M_{p}*F_{x}} + F_{y} - U_{y}} \right)}{\left( {M_{ubb} - M_{p}} \right)}},\;{R_{y} = {{M_{ubb}*\left( {R_{x} - U_{x}} \right)} + U_{y}}}$$Q_{x} = \frac{\left( {{M_{p}*E_{x}} - {M_{ubb}*U_{x}} + U_{y} - E_{y}} \right)}{\left( {M_{p} - M_{ubb}} \right)}$finds the x-coordinate of perpendicular intercept point Q Q_(y) =M_(ubb) * (Q_(x) − U_(x)) + U_(y) finds the y-coordinate ofperpendicular intercept point Q UR = {square root over((U_(x) − R_(x))² + (U_(y) − R_(y))²)}, QR = {square root over((R_(x) − Q_(x))² + (R_(y) − Q_(y))²)} finds the length of lines UR andQR ${FLP} = \frac{\overset{\_}{QR}}{UQ}$ finds the Forehead LengthProportion, here defined the perpendicular length of the foreheadrelative to the overal length of the head Descriptor Name: FaceThickness Proportion Anatomical Region of Face: AR01_FacialProfileDescribes the overall depth of the overall depth of the jaw relative tothe perpendicular height of the face. Indentifies a horse that is widefrom the mid-nose bone to the jaw.$M_{ubb} = \frac{\left( {U_{y} - {bb}_{y}} \right)}{\left( {U_{x} - {bb}_{x}} \right)}$finds the slope of the True Line of the Head M_(p) = −(M_(p))⁻¹ findsthe slope perpendicular to the True Line of the Head${{Qn}_{x} = \frac{\left( {{M_{ubb}*U_{x}} - {M_{p}*N_{x}} + N_{y} - U_{y}} \right)}{\left( {M_{ubb} - M_{p}} \right)}},{{Qn}_{y} = {{M_{ubb}*\left( {{QN}_{x} - U_{x}} \right)} + U_{y}}}$finds the x and y-coordinates of perpendicular intersect point Q_(N)${{Qc}_{x} = \frac{\left( {{M_{ubb}*U_{x}} - {M_{p}*C_{x}} + C_{y} - U_{y}} \right)}{\left( {M_{ubb} - M_{p}} \right)}},{{Qc}_{y} = {{M_{ubb}*\left( {{Qc}_{x} - U_{x}} \right)} + U_{y}}}$finds the x and y-coordinates of perpendicular intersect point Q_(C) NQn= {square root over ((N_(x) − Qn_(x))² + (N_(y) − Qn_(y))²)}, CQc ={square root over ((C_(x) − Qc_(x))² + (C_(y) − Qc_(y))²)},${FTP} = \frac{\overset{\_}{CQc}}{\overset{\_}{NQn}}$ finds the FaceThickness Proportion, here defined as the ratio between perpendicularheight of the face and the perpendicular thickness of the jaw DescriptorName: Nostril Position Proportion Anatomical Region of Face:AR01_FacialProfile Describes the angle at which the nose is set onto theface. Partially differentiates between a Roman Nose and Roman Head.Partially identifies a “Moose” nose.$M_{ac} = \frac{\left( {A_{y} - C_{y}} \right)}{\left( {A_{x} - C_{x}} \right)}$finds the slope of line N_(x) = B_(x) − (A_(x) − B_(x)) finds thex-coordinate for “imaginary” point N sufficiently far to the left ofpoint B to complete the triangle M_(y) = M_(ac) * (N_(x) − C_(x)) +C_(y) finds the y-coordinate of imaginary point N BN = {square root over((B_(x) − N_(x))² + (B_(y) − N_(y))²)}, CN = {square root over((C_(x) − N_(x))² + (C_(y) − N_(y))²)}, BC = {square root over((B_(x) − C_(x))2 + (B_(y) − C_(y))²)} finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{BN}}^{2} - {\overset{\_}{BC}}^{2} - {\overset{\_}{CN}}^{2}}{{- 2}*\overset{\_}{BC}*\overset{\_}{CN}} \right)}$finds the angle for ∠BCN, denoted here as θ BL = sin(θ) * BC finds theheight of triangle ΔCBN, defined here as the distance of displacement ofthe nose from the line of the upper face M_(bl) = −(M_(ac))⁻¹ finds theslope of the slope perpendicular to the line of the upper face${L_{x} = \frac{\left( {{M_{bl}*B_{x}} - {M_{ac}*A_{x}} + A_{y} - B_{y}} \right)}{\left( {M_{bl} - M_{ac}} \right)}},$finds the x-coordinate of the intersection point between the line of theupper face and its perpendicuklar bisector inclusive of point B$\sigma_{x} = \left( \frac{L_{x} - B_{x}}{\left| {L_{x} - B_{x}} \right|} \right)$finds the constant of nostril position with magnitude 1 indicatingdirection of position relative to the upper line of the face via sign(±)${NPP} = {\sigma_{u}*\left( \frac{\overset{\_}{BL}}{\overset{\_}{AB}} \right)}$finds the Nostril Position Proportion, here defined as the degree towhich the position of the nose varies from the upper line of the facerelative to the overall size of the face. Descriptor Name: ForeheadSlope Proportion Anatomical Region of Face: AR01_FacialProfile Describesthe slope of the forehead moving into the poll relative to the True Lineof the Head.$M_{ubb} = \frac{\left( {U_{y} - {bb}_{y}} \right)}{\left( {U_{x} - {bb}_{x}} \right)}$finds the slope of line UBb M_(p) = −(M_(ubb))⁻¹ finds the slope oflines EQ and FR perpendicular to line Ubb$Q_{x} = \frac{\left( {{M_{p}*E_{x}} - {M_{ubb}*U_{x}} + U_{y} - E_{y}} \right)}{\left( {M_{p} - M_{ubb}} \right)}$finds the x-coordinate of perpendicular intercept point Q Q_(y) =M_(ubb) * (Q_(x) − U_(x)) + U_(y) finds the y-coordinates ofperpendicular intercept point Q$R_{x} = \frac{\left( {{M_{p}*F_{x}} - {M_{ubb}*U_{x}} + U_{y} - F_{y}} \right)}{\left( {M_{p} - M_{ubb}} \right)}$finds the x-coordinates of perpendicular intercept point R R_(y) =M_(ubb) * (R_(x) − U_(x)) + U_(y) finds the y-coordinate ofperpendicular intercept point R EQ = {square root over((E_(x) − Q_(x))² + (E_(y) − Q_(y))²)}, FR = {square root over((F_(x) − R_(x))² + (F_(y) − R_(y))²)} finds the lengths of lines EQ andFR ${FSP} = \frac{\overset{\_}{FR}}{EQ}$ finds the Forehead SlopProportion, here defined as the ratio of perpendicular forehead heightfrom the True Line of the Head at the roastal and caudal-most points ofthe forehead Descriptor Name: Forehead Height Proportion AnatomicalRegion of Face: AR01_FacialProfile Describes the perpendicular height ofthe forehead above the True Line of the Face.$M_{AB} = \frac{\left( {B_{y} - A_{y}} \right)}{\left( {B_{x} - A_{x}} \right)}$finds the slope of line AB N_(x) = A_(x) + (A_(x) − B_(x)) finds thex-coordinate for “imaginary” point N sufficiently far to the right ofpoint A to complete the triangle N_(y) = M_(AB) * (N_(x) − A_(x)) +A_(y) finds the y-coordinate of imaginary point N BN = {square root over((B_(x) − N_(x))² + (B_(y) − N_(y))²)}, EN = {square root over((E_(x) − N_(x))² + (E_(y) − N_(y))²)}, BE = {square root over((S_(x) − E_(x))² + (B_(y) − E_(y))²)} finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{BE}}^{2} - {\overset{\_}{BN}}^{2} - {\overset{\_}{EN}}^{2}}{{- 2}*\overset{\_}{BN}*\overset{\_}{EN}} \right)}$finds the angle for ∠BNE, denoted here as θ DL = sin(θ) * EN finds theheight of triangle ΔCDN (perpendicular displacement of Forehead fromTrue Line of Face) M_(EL) = −(M_(AB))⁻¹ finds the slope of line EL$L_{x} = \frac{\left( {{M_{EL}*E_{x}} - {M_{AB}*A_{x}} + A_{y} - E_{y}} \right)}{\left( {M_{EL} - M_{AB}} \right)}$finds the x-coordinate of the perpendicular intersection point L$\sigma_{x} = \left( \frac{L_{x} - E_{x}}{\left| {L_{x} - E_{x}} \right|} \right)$finds the constant of forehead position with magnitude 1 indicatingdirection of position relative the true line of the face via sign (±)${FHP} = {\sigma_{u}*\left( \frac{\overset{\_}{DL}}{\overset{\_}{AB}} \right)}$finds the Forehead Height Proportion, here defined as the perpendiculardisplacement of the Upper Eye Orbital Point of the forehead from theTrue Line of the Face, relative to the length of the face. DescriptorName: Degree of Facial Inflexion Anatomical Region of Face:AR01_FacialProfile Describes the degree of structuralconcavity/convexity of the True Line of the Face. Differentiates betweena concave “dished”, convex “roman”, or straight bone structure. AB ={square root over ((B_(x) − A_(x))² + (B_(y) − A_(y))²)}, AC = {squareroot over (((C_(x) − A_(x))² + (C_(y) − A_(y))²)}, BC = {square rootover ((C_(x) − B_(x))² + (C_(y) − B_(y))²)} finds distance values forall sides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{BC}}^{2} - {\overset{\_}{AB}}^{2} - {\overset{\_}{AC}}^{2}}{{- 2}*\overset{\_}{AB}*\overset{\_}{AC}} \right)}$finds the angle for ∠BAC, denoted here as θ d = sin(θ) * AC finds theheight of triangle ΔABC, the distance of facial inflexion$M_{ab} = \frac{\left( {A_{y} - B_{y}} \right)}{\left( {A_{x} - B_{x}} \right)}$finds slope of line AB$Q_{x} = {\left( \frac{C_{y} - A_{y}}{M_{ab}} \right) + A_{x}}$ findsx-coordinate of point Q intersected by line AB and its horizontalbisector inclusive of point C σ = Qx − Cx finds a value for which σ iseither positive or negative, indicating respectively either convexity orconcavity by the location of point C relative to the position of point Qon the Profile Line of the face $\sigma_{u} = \frac{o}{|o|}$ finds u,here defined of the Constant of Concavity with magnitude 1 indicatingdirection of inflexion via sign (±)${DoFI} = {\sigma_{u}*\left( \frac{d}{\overset{\_}{AB}} \right)}$ findsDegree of Facial Inflexion, here defined as the degree of perpendiculardeviation of the Upper Profile of the skull from the True Line of theFace relative to ovrall length of face. Descriptor Name: Nose RoundnessProportion Anatomical Region of Face: AR01_FacialProfile Describes thedegree of roundness of the nasal bone as it results from the accretionof bone above the True Line of the Nose. Identifies convex “Moose” and“Roman” profiles. AB = {square root over((B_(x) − A_(x))² + (B_(y) − A_(y))²)}, AC = {square root over(((C_(x) − A_(x))² + (C_(y) − A_(y))²)}, BC = {square root over((C_(x) − B_(x))2 + (C_(y) − B_(y))²)} finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{BC}}^{2} - {\overset{\_}{AB}}^{2} - {\overset{\_}{AC}}^{2}}{{- 2}*\overset{\_}{AB}*\overset{\_}{AC}} \right)}$finds the angle ∠BAC, denoted here as θ d = sin(θ) * AC finds the heightof triangle ΔABC (distance of nostril rounding)$M_{ab} = \frac{\left( {A_{y} - B_{y}} \right)}{\left( {A_{x} - B_{x}} \right)}$finds the slope line of AB$Q_{x} = {\left( \frac{C_{y} - A_{y}}{M_{ab}} \right) + A_{x}}$ findsthe x-coordinate of point Q intersected by line AB and its horizontalbisector inclusive of point C σ = Q_(x) − C_(x) finds a value for whichσ is either positive or negative, indicating respectively eitherconvexity or concavity by the location of point C relative to theposition of point Q on the Profile Line of the face$\sigma_{u} = \frac{\sigma}{|\sigma|}$ finds σ_(u), here defined of theConstant of Concavity with magnitude 1 indicating direction ofinflexion/convection via sign (±)${DoFI} = {\sigma_{u}*\left( \frac{d}{\overset{\_}{AB}} \right)}$ findsthe Nose Roundness Proportion, here defined as the degree ofperpendicular deviation of the Upper Profile of the skull above the TrueLine of the Nose relative to the length of the nose. Descriptor Name:Nose_Length Proportion Anatomical Region of Face: AR01_FacialProfileDescribes the perpendicular length of a horse's nose relative to theoverall length of the face.$M_{ab} = \frac{\left( {A_{y} - B_{y}} \right)}{\left( {A_{x} - B_{x}} \right)}$finds the slope of the True Line of the Face M_(p) = −(M_(ab))⁻¹ findsthe slope perpendicular to the True Line of the Face$Q_{x} = \frac{\left( {{M_{p}*C_{x}} - {M_{ab}*A_{x}} + A_{y} - C_{y}} \right)}{\left( {M_{p} - M_{ab}} \right)}$finds the x-coordinate of perpendicular intercept point Q Q_(y) =M_(ab) * (Q_(x) − A_(x)) + A_(y) finds the y-coordinate of perpendicularintercept point Q AB = {square root over((A_(x) − B_(x))² + (A_(y) − B_(y))²)}, BQ = {square root over((B_(x) − Q_(x))² + (B_(y) − Q_(y))²)} finds the length of lines AB andBQ ${NLP} = \frac{\overset{\_}{BQ}}{\overset{\_}{AB}}$ finds the NoseLength Proportion, here defined the perpendicular length of the noserelative to the overall length of the face Descriptor Name: Degree ofFacial Protuberance Anatomical Region of Face: AR02_ProtuberancesDescribes the degree of prominence/enlargement of the sinus cavitiesabove the eyes (“Jibbah”) as they protrude from the underlying boneystructure of the True Line of the Face.$M_{Bc} = \frac{\left( {B_{y} - C_{y}} \right)}{\left( {B_{x} - C_{x}} \right)}$finds the slope of line BC N_(x) = C_(x) + (A_(x) − B_(x)) finds thex-coordinate for “imaginary” point N sufficiently far to the right ofpoint D to complete the triangle N_(y) = M_(EC) * (N_(x) − C_(x)) +C_(y) finds the y-coordinate of imaginary point N AC = {square root over((C_(x) − A_(x))² + (C_(y) − A_(y))²)}, CN = {square root over((C_(x) − N_(x))² + (C_(y) − N_(y))²)}, DN = {square root over((D_(x) − N_(x))² + (D_(y) − N_(y))²)}, CD = {square root over((D_(x) − C_(x))² + (D_(y) − C_(y))²)},$\theta = {{\cos^{- 1}\left( \frac{{\overset{\_}{CD}}^{2} - {\overset{\_}{CN}}^{2} - {\overset{\_}{ND}}^{2}}{{- 2}*\overset{\_}{CN}*\overset{\_}{ND}} \right)}\mspace{14mu}{finds}{\mspace{11mu}\;}{distance}\mspace{11mu}{values}\mspace{14mu}{for}{\mspace{11mu}\;}{all}\mspace{11mu}{sides}\mspace{14mu}{of}\mspace{14mu}{triangle}}$DL = sin(θ) * DN finds the angle for ∠CND, denoted here as θ finds theheight of triangle ΔCDN (perpendicular displacement of Sinus from TrueLine of the Nose) ${DoFP} = \frac{d}{\overset{\_}{AB}}$ finds the Degreeof Facial Protuberance, here defined as the perpendicular displacementof the Sinus Protuberence Point from the True Line of the Nose, relativeto the length of the upper portion of the face. Descriptor Name:Jowl-toUnderline Proportion Anatomical Region of Face: AR03_JowlSizeDecribes the magnitude of the lateral length of the jowl relative to thelength of the jaw bone. Partially differentiates between a large andsmall jowl. MN = {square root over((M_(x) − B_(x))² + (M_(y) − N_(y))²)} Finds the length of the underlineof the jaw NO = {square root over ((N_(x) − O_(x))² + (N_(y) − O_(y))²)}Finds the lateral length across the jowl JtUP = NO/MN Finds theJowl-to-Underline Proportion, here defined as the magnitude of thelateral length of the jowl relative to the length of jaw bone DescriptorName: Degree of Eye Orbital Protrusion Anatomical Region of Face:AR01_Facial Profile Describes the longitudinal placement of the eyeorbital on the top of the skull as it is positioned in correlation withthe sinus cavities. Partially identifies protruding sinus cavities.$m_{ab} = \frac{B_{y} - A_{y}}{B_{x} - A_{x}}$ finds the slope of lineAB m_(p) = −(m_(ab))⁻¹ Finds the slope perpendicular to the line AB$Q_{x} = \frac{{m_{ab}*A_{x}} - {m_{p}*C_{x}} + C_{y} - A_{y}}{m_{ab} - m_{p}}$finds x-coordinate of the intersection point between line AB and itsperpendicular bisector inclusive of point C Q_(y) =m_(ab)(Q_(x) − A_(x)) + A_(y) finds the y-coordinates of intersect point“Q”$P_{x} = \frac{{m_{ab}*A_{x}} - {m_{p}*D_{x}} + D_{y} - A_{y}}{m_{ab} - m_{p}}$finds the x-coordinate of intersection point between line AB andperpendicular bisector inclusive of point D P_(y) =m_(cb)(P_(x) − A_(x)) + A_(y) finds y-coordinate of intersect point “P”CQ = {square root over ((C_(x) − Q_(x))² + (C_(y) − Q_(y))²)} finds thedistance of displacement of the base point of the eye orbital from lineAB DP = {square root over ((D_(x) − P_(x))² + (D_(y) − P_(y))²)} findsthe distance of displacement of the tallest point of the eye orbitalfrom line AB AB = {square root over((B_(x) − A_(x))² + (B_(y) − A_(y))²)} finds the overall length of theface${DoEOP} = \frac{\overset{\_}{DP} - \overset{\_}{CQ}}{\overset{\_}{AB}}$finds Degree of Eye Orbital Protrusion, defined here as distance ofprotrusion of the maxima point of the eye orbital its base on the skullrelative to overall length of face Descriptor Name: Jowl ProtuberanceProportion Anatomical Region of Face: AR03_JowlSize Describes the degreeto which the minima point of the jowl protrudes from the underline ofthe jaw. Identifies shallow jowls. Partially differentiates betweenlarge and small jowls. $m_{mn} = \frac{M_{y} - N_{y}}{M_{x} - N_{x}}$Finds the slope of the underline of the jaw Q_(x) = N_(x) +2(N_(x) − M_(x)) Finds x-coordinate for imaginary point “q”,sufficiently far to the right of point N to complete a triangle on theline fit to the underline of the jaw Q_(y) = m_(mn)(Q_(x) − M_(x)) +M_(y) Finds the y-coordinate corresponding to imaginary point “q” NP ={square root over ((N_(x) − P_(x))² + (N_(y) − P_(y))²)}, PQ = {squareroot over ((P_(x) − Q_(x))² + (P_(y) − Q_(y))²)}, NQ = {square root over((N_(x) − Q_(x))² + (N_(y) − Q_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{PQ}}^{2} - {\overset{\_}{NQ}}^{2} - {\overset{\_}{NP}}^{2}}{{- 2}*\overset{\_}{NQ}*\overset{\_}{NP}} \right)}$Finds the angle measure ∠NQP, denoted here as θ d = sin θ * NP Finds theperpendicular distance from the line fit to the underline of the jaw tothe minima point of the jowl MN = {square root over((M_(x) − N_(x))² + (M_(y) − N_(y))²)} Finds the length of the underlineof the jaw JPP = σ/MN Finds the Jowl Protuberence Proportion, which heredescribes the length of protuberance of the jowl from the line of thejowl relative to the full length of the underline of the jaw DescriptorName: Jowl Roundness Proportion Anatomical Region of Face: AR03_JowlSizeDescribes degree of jowl rounding along the underline of the jowl asdescribed by the ratio of the perpendicular distance of the Mid JowlPoint from the True Line of the Jowl and the overall length of the jowl.MN = {square root over ((M_(x) − N_(x))² + (M_(y) − N_(y))²)} MK ={square root over ((M_(x) − K_(x))² + (M_(y) − K_(y))²)} NK = {squareroot over ((N_(x) − K_(x))² + (N_(y) − K_(y))²)} Finds distance valuesfor all sides of triangle$\theta = {\cos^{- 1}\left( \frac{{NK}^{2} - {MN}^{2} - {MK}^{2}}{{- 2}*{MN}*{MK}} \right)}$finds the angle for ∠NMK, denoted here as θ d = sin(θ) * MK finds theperpendicular depth of the jowl ${JRP} = \frac{d}{\overset{\_}{MN}}$finds the Jowl Roundness Proportion, here defined as the perpendiculardistance of rounding of the underline of the jowl from the True Line ofthe Jowl relative to the overall length of the jowl Descriptor Name:Muzzle Size Proportion Anatomical Region of Face: AR04_MuzzleShapeDescribes the overall size of the muzzle in terms of perpendicularheight relative to overall length of the face$M_{ubb} = \frac{\left( {U_{y} - {bb}_{y}} \right)}{\left( {U_{x} - {bb}_{x}} \right)}$finds the slope of the True Line of the Head M_(pu) = −(M_(ubb))⁻¹ findsthe slope perpendicular to the True Line of the Head${R_{x} = \frac{\left( {{M_{ubb}*U_{x}} - {M_{pu}*F_{x}} + F_{y} - U_{y}} \right)}{\left( {M_{ubb} - M_{pu}} \right)}},$R_(y) = M_(ubb) * (R_(x) − U_(x)) + U_(y) finds the x and y-coordinatesof intersect point R$M_{gh} = \frac{\left( {G_{y} - H_{y}} \right)}{\left( {G_{x} - H_{x}} \right)}$finds the slope of the True Line of the Chin M_(pk) = −(M_(gh))⁻¹ findsthe slope perpendicular to the True Line of the Chin$Q_{x} = \frac{\left( {{M_{gh}*G_{x}} - {M_{pb}*B_{x}} + B_{y} - G_{y}} \right)}{\left( {M_{gh} - M_{pb}} \right)}$Q_(y) = M_(ch) * (Q_(x) − G_(x)) + G_(y) finds the x and y-coordinatesof intersect point Q BQ = {square root over((B_(x) − Q_(x))² + (B_(y) − Q_(y))²)}, UR = {square root over((U_(x) − R_(x))² + (U_(y) − R_(y))²)} finds the distance values forlines BQ and UR ${MSP} = \frac{\overset{\_}{BQ}}{\overset{\_}{UR}}$finds the Muzzle Size Proportion, here defined as the perpendicularheight of the nuzzle relative to the overall length of the faceDescriptor Name: Mouth Inflexion Angle Anatomical Region of Face:AR04_MuzzleShape Describes the degree of inflexion/angulation taken bythe lower portion of the muzzle as it contracts inwards from the DistalNuzzle Point to the Distal Point of the Mouth. BS = {square root over((B_(x) − S_(x))² + (B_(y) − S_(y))²)}, BT = {square root over((T_(x) − B_(x))² + (T_(y) − B_(y))²)}, ST = {square root over((T_(x) − S_(x))² + (T_(y) − S_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{BT}^{2} - {BS}^{2} - {ST}^{2}}{{- 2}*{BS}*{ST}} \right)}$Finds angle value for Muzzle Slope Angle (∠ABC), denoted here as θDescriptor Name: Full Angle Face Anatomical Region of Face:AR04_MuzzleShape Describes both the magnitude and rate of change of thethikness of the face. Gives an indication of the relationhip between thejowl region of the face and the muzzle. Identifies a “teacup muzzle”.$m_{ab} = \frac{B_{y} - A_{y}}{B_{x} - A_{x}}$ Finds the slope of theTrue Line of Face $m_{cd} = \frac{D_{y} - C_{y}}{D_{x} - C_{x}}$ Findsthe slope of line fitted to underline of jaw$Q_{x} = \frac{{m_{ab}*A_{x}} - {m_{cd}*C_{x}} + C_{y} - A_{y}}{m_{ab} - m_{cd}}$Finds the x-coordinate of the point “Q” where the rue line of the faceand the line fitted to the underline of the jaw intersect Q_(y) =m_(ab)(Q_(x) − A_(x)) + A_(y) Finds the y-coordinate for intersect point“Q” AC = {square root over ((C_(x) − A_(x))² + (C_(y) − A_(y))²)}, AQ ={square root over ((A_(x) − Q_(x))² + (A_(y) − Q_(y))²)}, CQ = {squareroot over ((C_(x) − Q_(x))² + (C_(y) − Q_(y))²)} Finds distance valuesfor all sides of triangle${FAF} = {\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{AC}}^{2} - {\overset{\_}{AQ}}^{2} - {\overset{\_}{CQ}}^{2}}{{- 2}*\overset{\_}{AQ}*\overset{\_}{CQ}} \right)}}$Finds the angle measurement for Full Angle Face (∠AQC), here defined asthe intersect angle of the upper and lower lines of the face (True Lineof the Face & True Line of the Jaw) Descriptor Name: Muzzle RoundnessProportion Anatomical Region of Face: AR04_MuzzleShape Dexcribes thedegree of inflexion/angulation taken by the lower portion of the muzzleas it contracts inwards from the Distal Muzzle Point to the Distal Pointof the Mouth. ST = {square root over((T_(x) − S_(x))² + (T_(y) − S_(y))²)}, SU = {square root over((U_(x) − S_(x))² + (U_(y) − S_(y))²)}, UT = {square root over((T_(x) − U_(x))² + (T_(y) − U_(y))²)} finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{UT}}^{2} - {\overset{\_}{SU}}^{2} - {\overset{\_}{ST}}^{2}}{{- 2}*\overset{\_}{SU}*\overset{\_}{ST}} \right)}$finds the angle for ∠UST, denoted here as θ d = sin(θ) * EN finds theheight of triangle ΔSTU ${MRP} = \frac{d}{\overset{\_}{UT}}$ Finds theMuzzle Roundness Proportion, here defined as the perpendiculardisplacement of the Distal Muzzle Point from the line of the nuzzleformed by the Lower Nostril Point and Distal Mouth Point. DescriptorName: Forehead Height Angle Anatomical Region of Face: AR04_MuzzleShapeDescribes the angle of rise of the forehead along the topline of theface from the True Line of the Head.$m_{be} = \frac{B_{y} - E_{y}}{B_{x} - E_{x}}$ Finds the slope of theTrue Line of Face $m_{ubb} = \frac{U_{y} - {bb}_{y}}{U_{x} - {bb}_{x}}$Finds the slope of line fitted to underline of jaw$Q_{x} = \frac{{m_{be}*B_{x}} - {m_{ubb}*U_{x}} + U_{y} - B_{y}}{m_{be} - m_{ubb}}$Finds the x-ccordinate of the point “Q” where the true line of the faceand the line fitted to the underline of the jaw intersect Q_(y) =m_(be)(Q_(x) − B_(x)) + B_(y) Finds the y-coordinate for intersect point“Q” EQ = {square root over ((E_(x) − Q_(x))² + (E_(y) − Q_(y))²)}, Ebb ={square root over ((E_(x) − bb_(x))² + (E_(y) − bb_(y))²)}, Qbb ={square root over ((Q_(x) − bb_(x))² + (Q_(y) − bb_(y))²)} findsdistance values for all sides of triangle${FAF} = {\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{Ebb}}^{2} - {\overset{\_}{EQ}}^{2} - {\overset{\_}{Qbb}}^{2}}{{- 2}*\overset{\_}{EQ}*\overset{\_}{Qbb}} \right)}}$Finds the angle measure for Forehead Height Angle (∠EQB), here definedas the intersect angle of the topline of the face and the True Line ofthe Head Descriptor Name: Muzzle Slope Angle Anatomical Region of Face:AR04_MuzzleShape Describes the degree of slope/angulation taken by thetop line of the muzzle and upper lip. Identifies a “moose” nose.Partially differentiates between a round and square muzzle. BC = {squareroot over ((C_(x) − B_(x))² + (C_(y) − B_(y))²)}, BS = {square root over((B_(x) − S_(x))² + (B_(y) − S_(y))²)}, CS = {square root over((C_(x) − S_(x))² + (C_(y) − S_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{BS}^{2} - {BC}^{2} - {CS}^{2}}{{- 2}*{BC}*{CS}} \right)}$Finds angle valu for Muzzle Slope Angle (∠ABC), denoted here as θDescriptor Name: Zygomatic Ridge Angles Anatomical Region of Face:AR13_ZygomaticProces Describes the angle of the Zygomatic Ridge, fromthe point of the Zygomatic Process Point to the Distal Check Point,relative to the upper and lower lines of the face.$m_{yz} = \frac{Z_{y} - Y_{y}}{Z_{x} - Y_{x}}$ Finds the slope of theZygomatic Ridge $m_{ab} = \frac{B_{y} - A_{y}}{B_{x} - A_{x}}$ finds theslope of the True Line of the Face$m_{mn} = \frac{M_{y} - N_{y}}{M_{x} - N_{x}}$ Finds the slope of theLine of the Jaw$R_{x} = \frac{{m_{ab}*A_{x}} - {m_{yz}*Z_{x}} + Z_{y} - A_{y}}{m_{ab} - m_{yz}}$R_(y) = m_(ab)(R_(x) − A_(x)) + A_(y) finds the x-coordinate andy-coordinate of intersect point R$Q_{x} = \frac{{m_{mn}*N_{x}} - {m_{yz}*Z_{x}} + Z_{y} - N_{y}}{m_{mn} - m_{yz}}$Q_(y) = m_(mn)(Q_(x) − N_(x)) + N_(y) finds the x-coordinate andy-coordinate of intersect point Q BR = {square root over((B_(x) − R_(x))² + (B_(y) − R_(y))²)} NQ = {square root over((N_(x) − Q_(x))² + (N_(y) − Q_(y))²)} BY = {square root over((B_(x) − Y_(x))² + (B_(y) − Y_(y))²)} NY = {square root over((N_(x) − Y_(x))² + (N_(y) − Y_(y))²)} RY = {square root over((R_(x) − Y_(x))² + (R_(y) − Y_(y))²)} QY = {square root over((Q_(x) − Y_(x))² + (Q_(y) − Y_(y))²)} finds the distance values for alllegs of the triangles${ZRAU} = {\theta_{U} = {\cos^{- 1}\left( \frac{{BY}^{2} - {RB}^{2} - {RY}^{2}}{{- 2}*{BB}*{BY}} \right)}}$Finds the Zygomatic Ridge Angle_(Upper), here defined as the angkle ofthe Zygomatic Ridge bon as it relates to the True Line of the Face${ZRAL} = {\theta_{L} = {\cos^{- 1}\left( \frac{{\overset{\_}{NY}}^{2} - {\overset{\_}{QY}}^{2} - {\overset{\_}{QN}}^{2}}{{- 2}*\overset{\_}{QY}*\overset{\_}{QN}} \right)}}$finds the Zygomatic Ridge Angle_(lower), here defined as the angle ofthe Zygomatic Ridge bone as it relates to the Line of the Jaw DescriptorName: Top Eye Angle Anatomical Region of Face: AR11_EyeShape Describesdegree of angulation in the bone structure of the upper portion of theeye orbital. Differentiates between hard and soft eyes. Partiallyidentifies triangularly-shaped eyes. Abb = {square root over((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)} Acc = {square root over((cc_(x) − A_(x))² + (cc_(y) − A_(y))²)} bbcc = {square root over((cc_(x) − bb_(x))² + (cc_(y) − bb_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{Abb}}^{2} - {\overset{\_}{Acc}}^{2} - {\overset{\_}{bbcc}}^{2}}{{- 2}*\overset{\_}{Acc}*\overset{\_}{bbcc}} \right)}$Finds angle value for Top Eye Angle (∠ACB), denoted here as θ DescriptorName: Eye Size Proportion_(Length) Anatomical Region of Face:AR11_EyeShape Describes the size of the eye orbital in terms of lengthrelative to the scale of the face as a whole. AB = {square root over((B_(x) − A_(x))² + (B_(y) − A_(y))²)} Finds the length of the True Lineof the Face Abb = {square root over((A_(x) − bb_(x))² + (A_(y) − bb_(y))²)} Finds the lateral distance ofthe eye ESP_(L) = Abb/AB Finds the Eye Size Proportion_(Height) measure,which measures the size of the eye normalized the conserved distance ofthe True Line of the Face Descriptor Name: Eye Protuberance ProportionAnatomical Region of Face: AR11_EyeShape Describes the overall portionof the eye visible from a frontal view in terms of lateral protrusiondistance relative to the overall width of the forehead, which effectsthe range of a horse's frontal vision when set up into the bridle.$M_{ab} = \frac{\left( {A_{y} - B_{y}} \right)}{\left( {A_{x} - B_{x}} \right)}$finds the slope of the True Line of the Forehead M_(p) = −(M_(ab))⁻¹finds the slope perpendicular to the True Line of the Forehead$Q_{lx} = \frac{\left( {{M_{p}*C_{x}} - {M_{ab}*A_{x}} + A_{y} - C_{y}} \right)}{\left( {M_{p} - M_{ab}} \right)}$finds the X-coordinate of perpendicular intercept point Q_(L) Q_(ly) =M_(ab) * (Q_(lx) − A_(x)) + A_(y) finds the y-coordinate ofperpendicular intercept point Q_(L)$Q_{rx} = \frac{\left( {{M_{d}*D_{x}} - {M_{ab}*A_{x}} + A_{y} - D_{y}} \right)}{\left( {M_{p} - M_{ab}} \right)}$finds the x-coordinate of perpendicular intercept point Q_(L) Q_(ry) =M_(ab) * (Q_(rx) − A_(x)) + A_(y) finds the y-coordinate ofperpendicular intercept point Q_(L) AB = {square root over((A_(x) − B_(x))² + (A_(y) − B_(y))²)} finds the overall distance acrossthe forehead AQ_(l) = {square root over((A_(x) − Q_(lx))² + (C_(y) − Q_(ly))²)} finds the maximal lateraldistance of eye proportion of the left eye BQ_(r) = {square root over((A_(x) − Q_(rx))² + (C_(y) − Q_(ry))²)} finds the maximal lateraldistance of eye proportion of the right eye${EPP} = \frac{\overset{\_}{{AQ}_{l}} - \overset{\_}{{BQ}_{r}}}{2*\overset{\_}{AB}}$finds the Eye Protuberance Proportion, here defined as the averagedistance of lateral protrusion of the eye relative to the overalldistance across the forehead Descriptor Name: Eye Height ProportionAnatomical Region of Face: AR11_EyeShape Describes the perpendicularheight of the eye above the midline of the eye relative to the eyesoverall length. Abb = {square root over((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)} Acc = {square root over((cc_(x) − A_(x))² + (cc_(y) − A_(y))²)} bbcc = {square root over((cc_(x) − bb_(x))² + (cc_(y) − bb_(y))²)} Finds distance values for allside of triangle$\theta_{U} = {\cos^{- 1}\left( \frac{{\overset{\_}{Acc}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbcc}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{Bcc}} \right)}$finds the angle for ∠ABC, denoted here as θ d = sin(θ) * bbcc finds theperpendicular height of the eye ${EHP} = \frac{d}{\overset{\_}{Abb}}$finds the Eye Height Proportion, here defined as the ratio of theperpendicular height of the eye and the overall length of the eyeDescriptor Name: Eye Size Proportion_(Height) Anatomical Region of Face:AR11_EyeShape Describes the size of the eye orbital in terms of lengthrelative to the scale of the face as a whole. AB = {square root over((B_(x) − A_(x))² + (B_(y) − A_(y))²)} Finds the length of the True Lineof the Face Abb = {square root over((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)}, Acc = {square root over((cc_(x) − A_(x))² + (cc_(y) − A_(y))²)}, Add = {square root over((dd_(x) − A_(x))² + (dd_(y) − A_(y))²)}, bbcc = {square root over((cc_(x) − bb_(x))² + (cc_(y) − bb_(y))²)}, bbdd = {square root over((dd_(x) − bb_(x))² + (dd_(y) − bb_(y))²)} Finds distance values for allsides of triangle${\theta_{U} = {\cos^{- 2}\left( \frac{{\overset{\_}{Acc}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbcc}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{Bcc}} \right)}},$d_(U) = sin(θ) * bbdd finds the perpendicular height of the eye${\theta_{L} = {\cos^{- 2}\left( \frac{{\overset{\_}{Add}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbdd}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{bbdd}} \right)}},$d_(L) = sin(θ) * bbdd finds the perpendicular depth of the eye${ESP}_{H} = \frac{d_{U} + d_{L}}{\overset{\_}{AB}}$ finds the Eye SizeProportion_(Height), which measures the cumulative perpendicular heightof the eye relative to the length of the True Line of the FaceDescriptor Name: Uppr Maxima Point Proportion Eye Anatomical Region ofFace: AR11_EyeShape Describes the location of the highest point of theeye orbital relative to the lateral extrema of the eye. Identifiestriangular-shaped eyes. Abb = {square root over((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)} Acc = {square root over((cc_(x) − A_(x))² + (cc_(y) − A_(y))²)} bbcc = {square root over((cc_(x) − bb_(x))² + (cc_(y) − bb_(y))²)} Finds distance values for allsides of traingle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{Acc}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbcc}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{Bcc}} \right)}$Finds the angle measure ∠ABC, denoted here as θ d = cos θ * bbcc Findsthe distance between the right extrema point of the eye and theperpendicular intersect point of the upper maxima point of the eye UMPP= d/Abb Finds the Upper Maxima Point Preportion of the Eye, which ishere defined as the proportion of the lateral distance across the eyelocated bhind the perpendicular intersect of the upper maxima point ofthe eye Descriptor Name: Eye Roundness Proportion Anatomical Region ofFace: AR11_EyeShape Describes the perpendicular height of deviation ofthe Upper Media Eye Point from the Caudal Line of the Eye relative tothe ovrall length of the backside of the eye. bbcc = {square root over((bb_(x) − cc_(x))² + (bb_(y) − cc_(y))²)} bbee = {square root over((bb_(x) − ee_(x))² + (bb_(y) − ee_(y))²)} ccee = {square root over((cc_(x) − ee_(x))² + (cc_(y) − ee_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{bbee}}^{2} - {\overset{\_}{bbcc}}^{2} - {\overset{\_}{ccee}}^{2}}{{- 2}*\overset{\_}{bbcc}*\overset{\_}{ccee}} \right)}$finds the angle for ∠BCE, denoted here as θ d = sin(θ) * ccee finds theperpendicular roundness height of the eye ${ERP} = \frac{d}{bbcc}$ findsthe Eye Roundness Proportion, here defined as the perpendiculardeviation height of the Upper Media Eye Point relative to the overalllength of the Caudal Line of the Eye Descriptor Name: Lower Minima PointProportion Eye Anatomical Region of Fees: AR11_Eye Shape Describes thelocation of the lowest point of the eye orbital relative to the lateralextrema of the eye. Identifies “almond”-shaped eyes. Abb = {square rootover ((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)} Add = {square root over((dd_(x) − A_(x))² + (dd_(y) − A_(y))²)} bbdd = {square root over((dd_(x) − bb_(x))² + (dd_(y) − bb_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{Add}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbdd}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{bbdd}} \right)}$Finds the angle measure ∠ABD, denoted here as θ d = cosθ * bbdd Findsthe distance between the right extrema point of the eye and theperpendicular intersect point of the lower minima point of the eye UMPP= d/Abb Finds the Lower Minima Point Proportion of the Eye, which ishere defined as the proportion of the lateral distance across the eyelocated bhind the perpendicular intersect of the lower minima point ofthe eye Descriptor Name: Eye Height-to-Length Proportion AnatomicalRegion of Face: AR11_EyeShape Describes the cumulative perpendicularheight of the eye relative to the overall length of the eye. Abb ={square root over ((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)} Acc = {squareroot over ((cc_(x) − A_(x))² + (cc_(y) − A_(y))²)} Add = {square rootover ((dd_(x) − A_(x))² + (dd_(y) − A_(y))²)} bbcc = {square root over((cc_(x) − bb_(x))² + (cc_(y) − bb_(y))²)} bbdd = {square root over((dd_(x) − bb_(x))² + (dd_(y) − bb_(y))²)} Finds distance values for allsides of triangle$\theta_{U} = {\cos^{- 1}\left( \frac{{\overset{\_}{Acc}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbcc}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{Bcc}} \right)}$d_(U) = sin(θ) * bbdd finds the perpendicular height of the eye$\theta_{L} = {\cos^{- 1}\left( \frac{{\overset{\_}{Add}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbdd}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{bbdd}} \right)}$d_(L) = sin(θ) * bbdd finds the perpendicular depth of the eye${EHtLP} = \frac{d_{U} + d_{L}}{\overset{\_}{Abb}}$ finds the EyeHeight-to-Length Proportion, here defined as the ratio of the cumulativeperpendicular height of to the horizontal length of the eye DescriptorName: Eye Height Proportion Anatomical Region of Face: AR11-EyeShapeDescribes the perpendicular height of the eye above the midline of theeye relative to the eyes overall length. Abb = {square root over((bb_(x) − A_(x))² + (bb_(y) − A_(y))²)}, Add = {square root over((dd_(x) − A_(x))² + (dd_(y) − A_(y))²)}, bbdd = {square root over((dd_(x) − bb_(x))² + (dd_(y) − bb_(y))²)}, Finds distance values forall sides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{Add}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbdd}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{bbdd}} \right)}$finds the angle for ∠ABD, denoted here as θ d = sin(θ) * bbdd finds theperpendicular depth of the eye$\theta_{U} = {\cos^{- 1}\left( \frac{{\overset{\_}{Acc}}^{2} - {\overset{\_}{Abb}}^{2} - {\overset{\_}{bbcc}}^{2}}{{- 2}*\overset{\_}{Abb}*\overset{\_}{Bcc}} \right)}$finds the Eye Depth Proportion, here defined as the ratio of theperpendicular depth of the eye and the overall length of the eyeDescriptor Name: Eye Orbital Lateral Protuberance Proportion AnatomicalRegion of Face: AR11_EyeShape Describes the degree to which thelateral-most point of the boney portion of the eye orbital extendsbeyond the line of the cheek, which effects the range of a horses causaland ventral vision when set up into the bridle.$M_{oh} = \frac{\left( {E_{y} - H_{y}} \right)}{\left( {E_{x} - H_{x}} \right)}$finds the slope of the Line of the Cheek M_(o) = −(M_(eh))⁻¹ finds theslope perpendicular to the Line of the Cheek$Q_{x} = \frac{\left( {{M_{p}*G_{x}} - {M_{ab}*E_{x}} + E_{y} - G_{y}} \right)}{\left( {M_{p} - M_{ab}} \right)}$finds the x-coordinate of perpendicular intercept point Q Q_(y) =M_(eh) * (Q_(x) − E_(x)) + E_(y) finds the y-coordinate of perpendicularintercept point Q GQ = {square root over((G_(x) − G_(x))² + (G_(y) − G_(y))²)} finds the distance of eye orbitalprotrusion BF = {square root over ((B_(x) − F_(x))² + (B_(y) − F_(y))²)}finds the maximal lateral thickness of the cheeks${EOLPP} = \frac{\overset{\_}{GQ}}{\overset{\_}{EP}}$ finds the EyeOrbital Lateral Protuberance Proportion, here defined as the overallperpendicular distance of eye orbital displacement from the line of thecheek relative to the overall measure of cheek thickness DescriptorName: Eye Extrema Intersect Angle Anatomical Region of Face:AR11_EyeShape Describes the angle at which the upper and lower extremepoints of the eye are set against one another in relation to the TrueLine of the Eye.$M_{Abb} = \frac{\left( {A_{y} - {bb}_{y}} \right)}{\left( {A_{x} - {bb}_{x}} \right)}$finds the slope od line Abb$M_{ccdd} = \frac{\left( {{cc}_{y} - {dd}_{y}} \right)}{\left( {{cc}_{x} - {dd}_{x}} \right)}$finds the slope of line ccdd$Q_{x} = \frac{\left( {{M_{Abb}*A_{x}} - {M_{ccdd}*{cc}_{x}} + {cc}_{y} - A_{y}} \right)}{\left( {M_{Abb} - M_{ccdd}} \right)}$finds the x-coordinate of the intersect point Q Q_(y) = M_(Abb) *(Q_(x) − A_(x)) + A_(y) finds the x-coordinate of the intersect point QAQ = {square root over ((Q_(x) − A_(x))² + (Q_(y) − A_(y))²)} Acc ={square root over ((cc_(x) − A_(x))2 + (cc_(y) − A_(y))²)} Qcc = {squareroot over ((cc_(x) − Q_(x))² + (cc_(y) − Q_(y))²)} finds distance valuesfor all sides of triangle${{EXIA} - \theta} = {\cos^{- 1}\left( \frac{{\overset{\_}{Acc}}^{2} - {\overset{\_}{AQ}}^{2} - {\overset{\_}{Qcc}}^{2}}{{- 2}*\overset{\_}{AQ}*\overset{\_}{Qcc}} \right)}$finds the Eye Extrema Intersect Angle, here defined as angle θDescriptor Name: Forehead Width Angle Anatomical Region of Face:AR12_ForeheadWidth Describes the degree to which the forehead widensbeyond the nares (central line of the skull)$m_{ab} = \frac{B_{y} - A_{y}}{B_{x} - A_{x}}$ Finds the slope of theTrue Line of the Forehead, here defined as the line between the innerextrema points of the eyes m_(p) = −(m_(ab))⁻¹ Finds the slopeperpendicular to the True Line of the Forehead$R_{x} = \frac{{m_{ab}*A_{x}} - {m_{p}*C_{x}} + C_{y} - A_{y}}{m_{ab} - m_{p}}$Finds the x-coordinate of the intersection point between the true lineof the face and its perpendicular bisector incluive of point C R_(y) =m_(ab)(R_(x) − A_(x)) + A_(y) Finds the y-coordinate of the intersectionpoint “R”$S_{x} = \frac{{m_{ab}*A_{x}} - {m_{p}*D_{x}} + D_{y} - A_{y}}{m_{ab} - m_{p}}$Finds the x-coordinate of the intersection point between the true lineof the forehead and its perpendicular bisector inclusive of pint D S_(y)= m_(ab)(S_(x) − A_(x)) + A_(y) Finds the y-coordinate of intersectpoint “S” AC = {square root over ((C_(x) − A_(x))² + (C_(y) − A_(y))²)},AR = {square root over ((A_(x) − R_(x))² + (A_(y) − R_(y))²)}, BD ={square root over ((D_(x) − B_(x))² + (D_(y) − B_(y))²)}, BS = {squareroot over ((B_(x) − S_(x))² + (B_(y) − S_(y))²)} Finds distance valuesfor all sides of triangles$\theta_{R} = {\sin^{- 1}\left( \frac{AR}{AC} \right)}$ Finds the anglemeasure ∠ACR of forehead protuberance for the left side of the face$\theta_{S} = {\sin^{- 1}\left( \frac{BS}{BD} \right)}$ Finds the anglemeasure ∠BDS of forehead protuberance for the right side of the face FWA= (θ_(B) + θ_(S))/2 Finds the Forehead Width Angle, the average of thetwo angles of deviation of the extrema points of the eyes from thecenter line of the face Descriptor Name: Check-to-Zygomatic Height RatioAnatomical Region of Face: AR13_ZygomaticProces Describes the ratio ofCheek and Zygomatic Process height in terms of distance from the toplineof the face. $m_{yz} = \frac{Z_{y} - Y_{y}}{Z_{x} - Y_{x}}$ finds theslope of the Zygomatic Ridge$m_{ab} = \frac{A_{y} - B_{y}}{A_{x} - B_{x}}$ finds the slope oftheTrue Line of the Face $m_{yz} = \frac{Z_{y} - Y_{y}}{Z_{x} - Y_{x}}$finds the slope of the Line of the Forehead m_(p) = −(m_(p))⁻¹ finds theslope perpendicular to the Zygomatic Ridge$Q_{x} = \frac{{m_{ef}*E_{x}} - {m_{p}*Z_{x}} + Z_{y} - E_{y}}{m_{ef} - m_{p}}$Q_(y) = m_(ef)(Q_(x) − E_(x)) + E_(y) finds the x-coordinate andy-coordinate of intersect point Q$R_{x} = \frac{{m_{ab}*A_{x}} - {m_{p}*Y_{x}} + Y_{y} - A_{y}}{m_{ab} - m_{p}}$R_(y) = m_(ab)(R_(x) − A_(x)) + A_(y) finds the x-coordinate andy-coordinate of intersect point R RY = {square root over((R_(x) − Y_(x))² + (R_(y) − Y_(y))²)} finds the height of line RY, heredefined as the height of the cheek QZ = {square root over((Q_(x) − Z_(x))² + (Q_(y) − Z_(y))²)} finds the height of line RY, heredefined as the height of the Zygomatic Process CtZHP = RY/QZ Finds theCheek-to-Zygomatic Height Proportion, here defined as the ratio betweenthe distance from the Distal Cheek Point to the True Line of the Faceand the distance from the Zygomatic Process Point and the Line of theForehead Descriptor Name: Ear Inflexion Proportion Anatomical Region ofFace: AR09_EarShape Describes the degree to which the inner line of theear curves inward between the upper-most point of the ear and the medialpinna point.$M_{np} = \frac{\left( {N_{y} - P_{y}} \right)}{\left( {N_{x} - P_{x}} \right)}$finds the slope of line NP M_(p) = −(M_(np))⁻² finds the slopeperpendicular to line NP$Q_{x} = \frac{\left( {{M_{p}*J_{x}} - {M_{np}*N_{x}} + N_{y} - J_{y}} \right)}{\left( {M_{p} - M_{np}} \right)}$finds the x-coordinate of perpendicular intercept point Q Q_(y) =M_(np) * (Q_(x) − P_(x)) + P_(y) finds the y-coordinate of perpendicularintercept point Q QJ = {square root over((J_(x) − Q_(x))² + (J_(y) − Q_(y))²)} finds the distance of eye orbitalprotrusion PN = {square root over ((P_(x) − N_(x))² + (P_(y) − N_(y))²)}finds the maximal lateral thickness of the cheeks${EIP} = \frac{\overset{\_}{QJ}}{\overset{\_}{PN}}$ finds the EarInflexion Proportion, here defined as the perpendicular distance ofinflexion relative to the overall distance of concavity along the innerline of the ear Descriptor Name: Ear Width-to-Breadth ProportionAnatomical Region of Face: AR09_EarShape Describes the overall width ofthe broadcast section of the ear relative to its overall length. M_(x) =0.5(|K_(x) − J_(x)|) + J_(x), M_(y) = I_(y) = K_(y) finds the uppermid-ear point LM = {square root over((L_(x) − M_(x))² + (L_(y) − M_(y))²)} finds the length of the ear NH ={square root over ((N_(x) − H_(x))² + (N_(y) − H_(y))²)} finds the widthof the ear ${EIP} = \frac{\overset{\_}{NH}}{\overset{\_}{LM}}$ finds theEar Width-to-Breadth Proportion, here defined as the width of the ear atits broadest point relative to its overall length Descriptor Name:Degree of Ear Flare Anatomical Region of Face: AR09_EarShape Describesdegree to which the medical portion of the inner ear flares inwardbeyond the base structure of the ear. M_(x) = 0.5(|K_(x) − J_(x)|) +J_(x)* finds the upper mid-ear point M_(y) = I_(y) = K_(y)$m_{jt} = \frac{J_{y} - T_{y}}{J_{x} - T_{x}}$ finds the slope of theline JT M_(p) = −(M_(jt))⁻¹ finds the slope perpendicular to line JT$Q_{x} = \frac{\left( {{M_{p}*N_{x}} - {M_{jt}*J_{x}} + J_{y} - N_{y}} \right)}{\left( {M_{p} - M_{jt}} \right)}$Q_(y) = m_(ab) * (Q_(x) − A_(x)) + A_(y) finds the x and y-coordinatesof perpendicular intersect point Q NQ = {square root over((N_(x) − Q_(x))² + (N_(y) − Q_(y))²)} finds the perpendicular distanceof flaring LM = {square root over ((L_(x) − M_(x))² + (L_(y) − M_(y))²)}finds the overall length of the ear${DoEF} = \frac{\overset{\_}{NQ}}{\overset{\_}{LM}}$ finds the Degree ofEar Flare, here defined as the degree of perpendicular rise of themedial portion of the inner ear relative to the overall length of theear Descriptor Name: Chin Width-to-Height Proportion Anatomical Regionof Face: AR05_ChinShape Describes the ratio of the coverall length ofthe chin to its height at its minimal-most point. GH = {square root over((G_(x) − H_(x))² + (G_(y) − H_(y))²)}, GI = {square root over((G_(x) − I_(x))² + (G_(y) − I_(y))²)}, HI = {square root over((H_(x) − I_(x))² + (H_(y) − I_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{HI}}^{2} - {\overset{\_}{GI}}^{2} - {\overset{\_}{GH}}^{2}}{{- 2}*\overset{\_}{GI}*\overset{\_}{GH}} \right)}$finds the angle for ∠HGI, denoted here as θ QI = sin(θ) * GI finds thefinds the height of triangle ΔGHI (perpendicular height of the chin)${CWtHP} = \frac{\overset{\_}{QI}}{\overset{\_}{GH}}$ finds the ChinWidth-to-Height Proportion, here defined as the ratio of the length ofthe chin to its maximal perpendicular height Descriptor Name: ChinLength Angle Anatomical Region of Face: AR05_ChinShape Describes theoverall length of the chin relative to the degree to chich it protrudesfrom the underside of the jowl bone. Differentiates between a long andshort chin. Partially identifies a thin chin. GH = {square root over((G_(x) − H_(x))² + (G_(y) − H_(y))²)}, GI = {square root over((G_(x) − I_(x))² + (G_(y) − I_(y))²)}, HI = {square root over((H_(x) − I_(x))² + (H_(y) − I_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{GH}}^{2} - {\overset{\_}{GI}}^{2} - {\overset{\_}{HI}}^{2}}{{- 2}*\overset{\_}{GI}*\overset{\_}{HI}} \right)}$Finds angle value for Chin Length Angle (∠ACB), denoted as θ DescriptorName: Chin Firmness Proportion Anatomical Region of Face: AR05_ChinShapeDescribes the overall tightness of the chin, as reflected closeness ofthe Chin Maxima Point to the Caudal Chin Point relative to the overalllength of the chin$M_{gh} = \frac{\left( {G_{y} - H_{y}} \right)}{\left( {G_{x} - H_{x}} \right)}$finds the slope of line GH M_(qi) = −(M_(qi))⁻¹ finds the slope of lineQI, perpendicular to line GH$Q_{x} = \frac{\left( {{M_{gh}*G_{x}} - {M_{gi}*I_{x}} + I_{y} - G_{y}} \right)}{\left( {M_{gh} - M_{gi}} \right)}$finds the x-coordinate of the perpendicular intersection point Q Q_(y) =M_(gh) * (Q_(x) − G_(x)) + G_(y) finds the y-coordinate of intersectpoint Q GH = {square root over ((G_(x) − H_(x))² + (G_(y) − H_(y))²)},GQ = {square root over ((G_(x) − Q_(x))² + (G_(y) − Q_(y))²)} Findsdistance values for lines GH and GQ${CFP} = \frac{\overset{\_}{GQ}}{\overset{\_}{GH}}$ finds the ChinFirmness Proportion, here defined as the distance at which the ChinMinima Point is located from the front of the chin Descriptor Name: ChinThickness Angle Anatomical Region of Face: AR05_ChinShape Describes theposition of the minima point of chin relative to the point where thechin first becomes distinguishable from the lip. Diferentiates between arelaxed and tight chin. GH = {square root over((G_(x) − H_(x))² + (G_(y) − H_(y))²)}, GI = {square root over((G_(x) − I_(x))² + (G_(y) − I_(y))²)}, HI = {square root over((H_(x) − I_(x))² + (H_(y) − I_(y))²)} Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{HI}}^{2} - {\overset{\_}{GI}}^{2} - {\overset{\_}{GH}}^{2}}{{- 2}*\overset{\_}{GI}*\overset{\_}{GH}} \right)}$Finds the angle value for Chin Thickness Angle (∠BAC), denoted here as θDescriptor Name: Lip Length Proportion Anatomical Region of Face:AR05_ChinShape Describes the length of overhand of the lower lip as itcompares to the overall length of the chin.$M_{gh} = \frac{\left( {G_{y} - H_{y}} \right)}{\left( {G_{x} - H_{x}} \right)}$finds the slope of line GH M_(kq) = −(M_(kq))⁻¹ finds the slope of lineKQ, perpendicular to line GH$Q_{x} = \frac{\left( {{M_{gh}*G_{x}} - {M_{kq}*K_{x}} + K_{y} - G_{y}} \right)}{\left( {M_{gh} - M_{kq}} \right)}$finds the x-coordinate of the perpendicular intersection point Q Q_(y) =M_(gh) * (Q_(x) − G_(x)) + G_(y) finds the y-coordinate of intersectpoint Q HQ = {square root over ((H_(x) − Q_(x))² + (H_(y) − Q_(y))²)},GQ = {square root over ((G_(x) − Q_(x))² + (G_(y) − Q_(y))²)} Findsdistance values for lines HQ and KQ${LLP} = \frac{\overset{\_}{GQ}}{\overset{\_}{HQ}}$ finds the Lip LengthProportion, here defined as the length of overhang of the lower liprelative to the overall length of the chin Descriptor Name: ChinFullness Proportion Anatomical Region of Face: AR05_ChinShape Describesthe fullness/roundness of the chin as the perpendicular deviation of theprofile of the chin from the front line of the chin relative to theoverall length of the front line of the chin. GI = {square root over((G_(x) − I_(x))² + (G_(y) − I_(y))²)}, GJ = {square root over((G_(x) − J_(x))² + (G_(y) − I_(y))²)}, JI = {square root over((J_(x) − I_(x))² + (J_(y) − I_(y))²)} Finds distance values for allsides of the triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{JI}}^{2} - {\overset{\_}{GI}}^{2} - {\overset{\_}{GJ}}^{2}}{{- 2}*\overset{\_}{GI}*\overset{\_}{GJ}} \right)}$finds the angle for ∠GJI, denoted here as θ QI = sin(θ) * GI finds thefinds the height of triangle ΔGJI (perpendicular thickness height of thechin) ${CWtHP} = \frac{\overset{\_}{QJ}}{\overset{\_}{GI}}$ finds theChin Fullness Proportion, here defined as the perpendicular deviation ofthe Mid-Chin Point from the front line of the chin relative to theoverall length of the front line of the chin Descriptor Name: MouthLength Proportion Anatomical Region of Face: AR06_MouthLength Describesthe length of the mouth relative to the overall length of the head$M_{ubb} = \frac{\left( {U_{y} - {bb}_{y}} \right)}{\left( {U_{x} - {bb}_{x}} \right)}$finds the slope of the True Line of the Head M_(pu) = −(M_(ubb))⁻¹ findsthe slope perpendicular to the True Line of the Head$R_{x} = \frac{\left( {{M_{ubb}*U_{x}} - {M_{pu}*F_{x}} + F_{y} - U_{y}} \right)}{\left( {M_{ubb} - M_{pu}} \right)}$R_(y) = M_(ubb) * (R_(x) − U_(x)) + U_(y) finds the x and y-coordinatesof intersected point R JT = {square root over((J_(x) − T_(x))² + (J_(y) − T_(y))²)} finds the length of the mouth UR= {square root over ((U_(x) − R_(x))² + (U_(y) − R_(y))²)} finds thefull length of the head${MLP} = \frac{\overset{\_}{JT}}{\overset{\_}{UR}}$ finds the MouthLength Proportion, here defined as the length of the mouth relative tothe overall length of the head Descriptor Name: Lip ProtuberanceProportion Anatomical Region of Face: AR06_MouthLength Describes thedegree to which the lower lip protrudes beyond the distal-most point ofthe mouth. $m_{jt} = \frac{J_{y} - T_{y}}{J_{x} - T_{x}}$ finds theslope of the line of the mouth m_(p) = −(m_(jt))⁻¹ Finds the slopeperpendicular to the line of the mouth$Q_{x} = \frac{{m_{jt}*T_{x}} - {m_{p}*K_{x}} + K_{y} - T_{y}}{m_{jt} - m_{p}}$finds the x-coordinate of perpendicular intersect point Q Q_(y) =m_(j)t(Q_(x) − T_(x)) + T_(y) finds the y-coordinate of intersect point“Q” JQ = {square root over ((J_(x) − Q_(x))² + (J_(y) − Q_(y))²)} findsthe total length of the lower lip TQ = {square root over((T_(x) − Q_(x))² + (T_(y) − Q_(y))²)} finds the distance which thedistal-most point of the lower lip protrudes beyond the distal-mostpoint of the mouth$\sigma_{u} = \left( \frac{Q_{x} - T_{x}}{\left| {Q_{x} - T_{x}} \right|} \right)$finds the constant of lip position with magnitude 1 indicating theposition of the lip beyond or behind the distal-most point of the mouth${LPP} = {\sigma_{u}*\left( \frac{\overset{\_}{TQ}}{\overset{\_}{JQ}} \right)}$finds the Lip Protuberance Proportion, which here defines the positionof the distal-most point of the lower lip relative to the distal mostpoint of the mouth Descriptor Name: Nostril Length Proportion AnatomicalRegion of Face: AR07_NostrilShape Describes the lateral length acrossthe nostril relative to the overall length of the upper lip. BU ={square root over ((B_(x) − U_(x))² + (B_(y) − U_(y))²)} Finds thelength of the nostril UT = {square root over((U_(x) − T_(x))² + (U_(y) − T_(x))²)} Finds the length of the upper lipNWP = BU/UT Finds the Nostril Length Proportion, here defined at thelateral length of the nostril relative to the overall length of theupper lip Descriptor Name: Degree of Nostril Flutendess AnatomicalRegion of Face: AR07_NostrilShape Describes the degree to which excessskin along the inside of the nostril extends and curls around the innerline of the nostril.$M_{xs} = \frac{\left( {X_{y} - S_{y}} \right)}{\left( {X_{x} - S_{x}} \right)}$finds the slope of the Line of the Inner Nostril M_(p) = −(M_(xs))⁻¹finds the slope perpendicular to the Line of the Inner Nostril$Q_{x} = \frac{\left( {{M_{xs}*S_{x}} - {M_{p}*Z_{x}} + Z_{y} - S_{y}} \right)}{\left( {M_{xs} - M_{u}} \right)}$Q_(y) = M_(ty) * (Q_(x) − T_(x)) + T_(y) finds the x and y-coordinatesof intersect point Q XS = {square root over((X_(x) − S_(x))² + (X_(y) − S_(y))²)} finds the overall length of thenostril ZQ = {square root over ((Z_(x) − Q_(x))² + (Z_(y) − Q_(y))²)}finds the perpendicular distance of rise of the fleshy skin flap of theinner nostril from the Line of the Inner Nostril${DoNF} = \frac{\overset{\_}{ZQ}}{\overset{\_}{XS}}$ finds the Degree ofNostril Flutedness, here defined as the distance of inflexion of theskin of the inner nostril from the Line of the Inner Nostril (the fluteof the nose) relative to the overall length of the nostril DescriptorName: Nose Width-to-Height Proportion Anatomical Region of FAce:AR07_NostrilShape Describes the perpendicular height of the noserelative to the overall breadth of the nose.$M_{xy} = \frac{\left( {X_{y} - Y_{y}} \right)}{\left( {X_{x} - Y_{x}} \right)}$finds the slope of the Upper Line of the Nostril M_(p) = −(M_(xy))⁻¹finds the slope perpendicular to the Upper Line of the Nostril$Q_{x} = \frac{\left( {{M_{xy}*Y_{x}} - {M_{p}*S_{x}} + S_{y} - Y_{y}} \right)}{\left( {M_{xy} - M_{u}} \right)}$Q_(y) = M_(xy) * (Q_(x) − X_(x)) + X_(y) finds the x and y-coordinatesof intersect point Q XY = {square root over((X_(x) − Y_(x))² + (X_(y) − Y_(y))²)} finds the overall breadth of thenostril QS = {square root over ((S_(x) − Q_(x))² + (S_(y) − Q_(y))²)}finds the perpendicular height of the nose${DWtHP} = \frac{\overset{\_}{QS}}{\overset{\_}{XY}}$ finds the NoseWidth-to-Height Proportion, here defined as the ratio between theperpendicular height of the nostrils and the overall breadth of the noseDescriptor Name: Degree of Nostril Roundness Anatomical Region of Face:AR07_NostrilShape Describes the perpendicular height of the outer edgeof the nostril above the line of the innr nostril. Identifies horseswith large and/or flared nostrils.$M_{ty} = \frac{\left( {T_{y} - Y_{y}} \right)}{\left( {T_{x} - Y_{x}} \right)}$finds the slope of the Line of the Inner Nostril M_(p) = −(M_(ty))⁻¹finds the slope perpendicular to the Line of the Inner Nostril$Q_{x} = \frac{\left( {{M_{ty}*T_{x}} - {M_{p}*V_{x}} + V_{y} - T_{y}} \right)}{\left( {M_{ty} - M_{u}} \right)}$Q_(y) = M_(ty) * (Q_(x) − T_(x)) + T_(y) finds the x and y-coordinatesof intersect point Q TY = {square root over((T_(x) − Y_(x))² + (T_(y) − Y_(y))²)} finds the overall length of thenostril VQ = {square root over ((V_(x) − Q_(x))² + (V_(y) − Q_(y))²)}finds the perpendicular width of the nostril at its widest point${DoNR} = \frac{\overset{\_}{VQ}}{\overset{\_}{TY}}$ finds the Degree ofNostril Roundness, here defined as the perpendicular width of thenostril at its widest point elative to the overall length of the nostrilDescriptor Name: Inner-Nostril Convergence Proportion Anatomical Regionof Face: AR07_NostrilShape Describes the horizontal positions of theupper and lower medial points of the nostrils relative to one another asthey align with the mid-line of the face. Differentiated between horseswith flat vertically aligned nostrils and horses with highly curvedangled nostrils. ST = {square root over((S_(x) − T_(x))² + (S_(y) − T_(y))²)} Finds lateral distance across theupper bridge of the nose XY = {square root over((X_(x) − Y_(x))² + (X_(y) − Y_(x))²)} Finds lateral distance across thelower bridge of the nose INCP = ST/XY Finds the Inner-NostrilConvergence Proportion, as the ratio between the lateral distancebetween the upper and lower portions of the bridge of the noseDescriptor Name: Nostril Width Proportion Anatomical Region of Face:AR07-NostrilShape ST = {square root over((S_(x) − T_(x))² + (S_(y) − T_(y))²)} Finds lateral distance acrossmuzzle non-inclusive of nostril area UV = {square root over((U_(x) − V_(x))² + (U_(y) − V_(x))²)} Finds total lateral distanceacross the muzzle NWP = ST/UV Finds the Nostril Width Proportion, heredefined as the lateral distance of the bridge of the nose relative tothe overall distance across the muzzle inclusive of nostrils DescriptorName: Degree of Lip Inflexion Anatomical Region of Face: AR08_LipShapeDescribes degree of inflexion/curvature of upper lip. Differentiatesbetween flat and “heart-shaped” upper lip. AB = {square root over((B_(x) − A_(x))² + (B_(y) − A_(y))²)}, AC = {square root over((C_(x) − A_(x))² + (C_(y) − A_(y))²)}, BC = {square root over((C_(x) − B_(x))² + (C_(y) − B_(y))²)}, Finds distance values for allsides of triangle$\theta = {\cos^{- 1}\left( \frac{{\overset{\_}{BC}}^{2} - {\overset{\_}{AB}}^{2} - {\overset{\_}{AC}}^{2}}{{- 2}*\overset{\_}{AB}*\overset{\_}{AC}} \right)}$Finds the angle measure for ∠BAC, denoted here as θ d = sinθ * AC Findsthe distance “d” of inflexion DoLP = d/AB Finds Degree of Lip Inflexion,which is here defined to the distance of medial inflexion of the liprelative to the overall width of the lips Descriptor Name: Ear RoundnessProportion Anatomical Region of Face: AR09_EarShape Describes the degreeto which the tip of the ear curves inward away from the midline of theear. This measure distinguishes between curvatures at the top of the eardue to cancavity at the tip and curvature due to actual rounding of theear structure. M_(x) = 0.5(|K_(x) − J_(x)|) + J_(x)*, M_(y) = I_(y) =K_(y) finds the upper mid-ear point$m_{ml} = \frac{M_{y} - L_{y}}{M_{x} - L_{x}}$ finds the slope of theMidline of the Ear M_(p) = −(M_(ml))⁻¹ finds the slope perpendicular tothe Midline of the Ear$Q_{x} = \frac{\left( {{M_{p}*P_{x}} - {M_{ml}*L_{x}} + L_{y} - P_{y}} \right)}{\left( {M_{p} - M_{ml}} \right)}$Q_(y) = M_(ab) * (Q_(x) − A_(x)) + A_(y) finds the x and y-coordinatesof intersect point Q PQ = {square root over((P_(x) − Q_(x))² + (P_(y) − Q_(y))²)} finds the perpendicular distanceof rounding at the tip of the ear LM = {square root over((L_(x) − M_(x))² + (L_(y) − M_(y))²)} finds the overall length of theear ${ERP} = \frac{\overset{\_}{QJ}}{\overset{\_}{PN}}$ finds the EarRoundness Proportion, here defined as the perpendicular distance ofinward curvature of the tip of the ear from the midline of the ear,reltive to the overall length of the ear Descriptor Name: Ear RotationProportion Anatomical Region of Face: AR10_EarPosition Describes thedegree of rotation of the ear as it is naturally set onto the forehead.Ears that are set wider apart and therefore farther down the poll arerotated laterally and show from the frontal view lesser pinna area. Thismeasure reflects the proportion of area located at the base of the earvisually identified as pinna, differentiating between horses with wideand narrow set ears. $m_{ab} = \frac{B_{y} - A_{y}}{B_{x} - A_{x}}$Finds the slope of the True Line of the Forehead, here defined as theline between the inner extrema points of the eyes m_(p) = −(m_(ab))⁻¹Finds the slope perpendicular to the True Line of the Forehead$N_{x} = {{\frac{{m_{ab}*A_{x}} - {m_{p}*C_{x}} + C_{y} - A_{y}}{m_{ab} - m_{p}}\mspace{31mu} P_{x}} = \frac{{m_{ab}*A_{x}} - {m_{p}*D_{x}} + D_{y} - A_{y}}{m_{ab} - m_{p}}}$$Q_{x} = \frac{{m_{ab}*A_{x}} - {m_{p}*E_{x}} + E_{y} - A_{y}}{m_{ab} - m_{p}}$FInds the x-coordinates of the intersection points between the true lineof the forehead and its perpendicular bisector incluive of pint C, D,and E respectively N_(y) = m_(ab)(N_(x) − A_(x)) + A_(y) P_(y) =m_(ab)(P_(x) − A_(x)) + A_(y) Q_(y) = m_(ab)(Q_(x) − A_(x)) + A_(y) Findthe y-coordinated corresponding to intersect points N, P, and Q NP ={square root over ((P_(x) − N_(x))² + (P_(y) − N_(y))²)} Finds thedistance across the base of the ear non-inclusive of pinna NQ = {squareroot over ((Q_(x) − N_(x))² + (Q_(y) − N_(y))²)} Finds the totaldistance across the base of the ear ERP = NP/NQ Finds the Ear RotationProportion, here defined as the proportion of the base of the earnon-inclusive of pinna as a indication of degree of rotation andwideness of set Descriptor Name: Ear Set Angle Anatomical Region ofFace: AR10_EarPosition Describes the angle at which the ears arepositioned onto the top of the skull, reflecting the natural position ofthe ears when the horse is in an emotionally neutral state of mind.Differentiates between wide, narrow, and vertically places ears. Whenthe anatomical reference points are manually selected, the mostappropriate to use must be chosen based on how clearly the anatomicalreference point can be seen and by which ear shows the highest degree offorward rotation.. The point on the inside curvature of the ear shouldbe at the medial-most point of the curve, and the point of the outsidecurvature of the ear should be traced over so that the horizontalcross-hairs remain aligned horizontally with the inner ear point. M_(x)= 0.5(|K_(x) − J_(x)|) + J_(x)* finds the upper mid-ear point M_(y) =I_(y) = K_(y) $m_{ml} = \frac{M_{y} - L_{y}}{M_{x} - L_{x}}$ finds theslope of the Midline of the Eare$Q_{x} = \frac{\left( {{M_{ab}*A_{x}} - {M_{ml}*L_{x}} + L_{y} - A_{y}} \right)}{\left( {M_{ab} - M_{ml}} \right)}$finds the x and y-coordinates of intersect point Q Q_(y) = M_(ab) *(Q_(x) − A_(x)) + A_(y) AB = {square root over((A_(x) − B_(x))² + (A_(y) − B_(y))²)}, AM = {square root over((M_(x) − A_(x))² + (M_(y) − A_(y))²)}, MQ = {square root over((M_(x) − Q_(x))² + (M_(y) − Q_(y))²)} Finds the lengths of all sides ofthe triangle${{ESA} = {\theta = {\cos^{- 1}\left( \frac{{AM}^{2} - {AB}^{2} - {MQ}^{2}}{{- 2}*{AB}*{MQ}} \right)}}}\;$Finds the Ear Set Angle, defined here as the angle at which the midlineof the ear meets the True Line of the Face

Nevertheless, these anatomical regions and measurements are exemplaryrather than exhaustive, as others could be added to or removed from theset within the spirit and scope of the disclosure. Moreover, althoughFIGS. 1 through 13 show anatomical regions of the equine face,embodiments may be applied to other equine anatomical regions or toanatomical regions of other domesticated animals, such as donkeys,cattle, oxen, llamas, sheep, goats, dogs, camels, geese, chickens,turkeys, cats, and pigs.

FIG. 15 shows an embodiment in the form of a method for determining apredictor of a characteristic of a particular type of animal. As usedherein, “type” refers to breed, species, sub-species, or any otherrelevant genetic similarity. In step 1500, a sample library is createdfor the type of animal of interest. This step comprises obtaining one ormore digital images of an anatomical region for each of a plurality ofanimals of interest, e.g., an equine face. The image may be obtained invarious ways, such as from a memory card of a digital camera, bydownloading via File Transfer Protocol (FTP), via email, etc. Onceobtained, the images are stored in a memory operably connected to adigital computer, such as memory that is either locally or remotelyaccessible by the computer, including an attached hard drive, removableflash drive, network drive, RAID (redundant array of independent disks)drive, removable memory card, etc.

Step 1500 also comprises obtaining additional data related to thecharacteristic of the particular type of animal and storing it in thesame or a different memory operably connected to the digital computer.As used herein, “data” may refer to performance records, vitalstatistics, and/or any other quantitative information that is related insome way to the characteristic of interest. While qualitative data mayalso be used, in many embodiments the qualitative data is converted intoquantitative data for easier use by the system. The additional data maypertain to individual animals in the sample library, groups of animalsin the sample library, or more generally the type of animal. Theadditional data is stored in a manner and location by which it can beassociated with the sample library images, such as in a relationaldatabase accessible by the digital computer.

In an exemplary embodiment, the animal of interest is an Arabian horseand the characteristic of interest is whether the animal is best suitedfor the English Pleasure (EP), Western Pleasure (WP), or Working Western(WW) riding discipline. For this purpose, a sample library of digitalimages of Arabian horses may be collected, together with additional datarelating to their known riding disciplines. The number of horses in thesample library may be chosen based on availability and desiredstatistical accuracy of the predictor, as readily understood by personsof ordinary skill in the art. In the exemplary embodiment, the samplelibrary comprises 81 Arabian horses, each with annotated images andinformation about their riding discipline.

In step 1502, a plurality of reference points are added to the one ormore stored images of a particular animal in the sample library. Thismay be accomplished in an automated fashion or manually, for example byuse of a program with a graphical user interface (GUI) running on thedigital computer. For example, the one or more images can be processedby MATLAB, an advanced mathematical analysis program sold by TheMathworks, Inc., of Natick, Massachusetts (http://www.mathworks.com).MATLAB provides advanced image processing features and flexible optionsfor definition of large numbers of variables, specifically matrices.Reference points are added to each of the images by using the MATLAB“GInput” command, which provides an interactive selection GUI. In someembodiments, reference points are manually selected on the image. Onesuch embodiment is the exemplary equestrian embodiment, in which FIGS. 1through 13 were manually annotated with reference points (e.g., pointsA, B, and C in FIG. 1A). In other embodiments, reference points may beadded automatically by MATLAB or another software application based on ageneric model of the animal's face. Once the reference points areentered onto an image, MATLAB maps their pixel locations within theimage to numerical coordinates within the corresponding matrix tofacilitate further computation.

In step 1504, one or more facial descriptor measurements (FDMs) relatedto the characteristic of interest are computed from the set of referencepoints that were added to the one or more digital images of the animal.In the exemplary equestrian embodiment, the facial descriptormeasurements may be computed using distance measurements andtrigonometric functions as illustrated in FIGS. 1-13. Because lengthdistance measurements are based on coordinate positions within the pixelmatrix, the absolute distance values may be sensitive to factors such ascamera resolution, artifacts of one or more compressions of the image,and cropping applied to isolate the face. To overcome such factors, thelength measurements may be normalized to structural reference lengthsthat are effectively constant among animals of the same type and subjectto the same set of factors. For example, in FIG. 1A, the facialinflection distance, d, is normalized by the overall length of thefacial profile, AB. However, it is apparent to one of ordinary skillthat the facial descriptor measurements may be based upon absolute ornon-normalized length measurements if the factors described above werenot present or were not a concern. In other embodiments, one or more ofthe facial descriptor measurements may be based on an angularmeasurement or an area measurement. The facial descriptor measurementsmay be based on non-trigonometric calculations, such integral calculuscalculations, or a combination of trigonometric and non-trigonometriccalculations.

In some embodiments, one or more of the digital images of the animal arethree-dimensional images. Such images may be created by combiningmultiple two-dimensional images of the animal using stereophotogrammetryor other methods known to persons of ordinary skill in the art. In suchembodiments, one or more of the facial descriptor measurements may bebased on a three-dimensional measurement, such as an absolute volume, avolumetric ratio, a solid angle, or a combination thereof.

As shown in FIG. 15, steps 1502 and 1504 are repeated for each animal inthe sample library. Once complete, in step 1508, a relationship isdetermined between a particular facial descriptor measurement and theadditional data related to the characteristic of interest using allanimals in the sample library. For example, the relationship can bedetermined from the mathematical correlation between the facialdescriptor measurement and additional data for all animals in the samplelibrary. The correlation may be normalized or scaled as necessary tomake it meaningful for further computation or interpretation. Othermeasures can be used to determine the effectiveness of a facialdescriptor measurement in the model. For example, in categorical models(e.g., those used to predict riding discipline), ROC curve analysis maybe used to select indicator of categorical potential, as describedfurther below. Multi-dimensional, Euclidean distance analysis also maybe used to separate two groups categorically. Other methods fordetermining a relationship based on appropriate statistical models areapparent to persons of ordinary skill in the art. In the exemplaryequestrian embodiment, the relationship is based on the correlationbetween the facial descriptor measurement and the known ridingdiscipline. As illustrated in FIG. 15, step 1506 is repeated for eachfacial descriptor measurement.

In step 1512, one or more of the facial descriptor measurements areselected to be used as predictors of the characteristic of interest. Anynumber of facial descriptor measurements—up to and including the entireset—may be selected. If there are multiple characteristics of interest,then a separate selection may be made for each characteristic. Theselection may be made in various ways, depending on the availableinformation and the characteristic of interest. Moreover, in step 1512,the combination of the selected facial descriptor measurements thatoptimizes the predictor is also determined. For example, an optimallinear combination of the selected subset of facial descriptormeasurements is determined using statistical analysis techniques. In thespirit of the disclosure, however, a non-linear combination of theentire set, or a selected subset, of the facial descriptor measurementsalso may be determined from the sample library. If multiplecharacteristics are of interest, then an optimal combination for eachcharacteristic may be selected.

In the exemplary equestrian embodiment, facial descriptor measurementsmay be selected for predicting whether an Arabian horse is best suitedfor the English Pleasure (EP), Western Pleasure (WP), or Working Western(WW) riding discipline. Based on the categorical power of the facialdescriptor measurements, ROC (receiver operating characteristic)analysis can be used to select facial descriptor measurement(s) thatbest categorizes the sample of horses into their actual ridingdisciplines. FIG. 21 shows an exemplary ROC curve, where the falsepositive rate is shown on the horizontal axis and the true positive rateis shown on the vertical axis. The ROC curve shown in FIG. 21 has anarea of 0.801; a ROC curve area greater than 0.65 indicates that aparticular facial descriptor measurement is useful for prediction orcategorization.

In the subset selection of the exemplary embodiment, two sets of ROCcurves were generated: one containing binomial separations (A vs. B) andthe other single discipline isolations from the full population (A vs.B+C). The discipline combination among the isolation curves with thehighest separation values was selected for use in the first tier of theclassification model. The remaining two groups were then separatedbinomially in the second tier of the model. For each classificationlevel of the model, the two individual facial descriptor measurementswith the highest ROC scores were selected at the optimization variables.The strongest set of isolation ROC curves chosen to comprise the firsttier of the model was the WW separation using descriptor values forAR03_Jowl Protuberance Proportion (ROC area=0.819) andAR03_Jowl-to-Underline Proportion (ROC area=0.817) as optimizationvariables. The second tier of the model separated binomially the EP andWP groups, utilizing descriptor values for AR10_Ear Rotation Proportion(ROC area=0.863) and AR11_Eye Size Proportion (ROC area=0.755).

At each tier of the model, the z-scores (also commonly known as standardscores or z-values) of the facial descriptor measurements selected asthe optimization variables were linearly combined. Coefficients forthese variables, in addition to the cutoff value itself, were thenoptimized on a training set consisting of eleven randomly selectedhorses in the sample library from each of the three riding disciplines.The optimization resulted in linear equations capable of producingdiscipline descriptor values that would accurately categorize thehighest possible number of horses into their known disciplines. Theoptimized linear equations and cutoff were then used on the remaining 48horses in the 81-horse sample library to validate the accuracy of themodel. FIG. 18 shows the results of subset selection process as appliedto the 81-horse sample library, resulting in selection of a linearcombination of two of the facial descriptor measurements. The describedmethod of selecting an optimal linear combination of the selected facialdescriptor measurements is merely exemplary. In an alternate embodiment,the linear combination coefficients may be selected using the entiresample library. In the same manner, a non-linear combination of theselected facial descriptor measurements may be selected usingoptimization techniques including, but not limited to, Newton's Methodand Lagrange's Method.

According to the embodiment illustrated by FIG. 16, the selected subsetand combination of facial descriptor measurements then can be used topredict a characteristic of an animal based on the facial descriptormeasurements for that animal. In other words, the subset and combinationselected based on the sample library can be applied to other animals ofthe same type to determine the characteristic for those animals. In step1600, digital images and additional data is obtained and stored for ananimal of interest, in the same manner as described above with referenceto the sample library (i.e., step 1500 of FIG. 15). In step 1602,reference points consistent with those of the images in the samplelibrary are added to the images of the animal of interest. In step 1604,facial descriptor measurements are calculated for the animal ofinterest. In the exemplary equestrian embodiment, the reference pointsand facial descriptor measurements are illustrated by FIGS. 1 through13.

In step 1606, the subset and combination selected in step 1512 isapplied to the facial descriptor measurements of the animal to predictthe characteristic of interest. By way of example, the subset andcombination shown in FIG. 18 is applied to the facial descriptormeasurements of an Arabian horse to determine whether that animal isbest suited for EP, WP, or WW riding discipline. In step 1608, thesample library optionally may be augmented by adding the image(s),additional data, and facial descriptor measurements for this animal.Subsequently, the method of FIG. 15 can be repeated using the augmentedsample library, and the resulting predictor can be applied to additionalanimals of the same type in accordance with the method of FIG. 16.

Using the method illustrated by FIG. 15, facial descriptor measurementsalso may be selected for use in determining the performance of aparticular horse in a desired riding discipline. Few accurate measuresof performance exist within the equestrian industry. One exemplarymeasure of competitive performance is win percentile scores, heredefined as the percentage of horses that a given horse was able to beatat point qualifying competition in a given season out of the totalnumber of horses shown against. For each of the 81 horses, winpercentiles were calculated for every season with the best winpercentile retained. In one embodiment, selection of the subset offacial descriptor measurements is done by finding r-squared valuesrepresenting the correlation between win percentiles and each of thefacial descriptor measures for each discipline. For each ridingdiscipline, the four descriptor values with the highest correlationswere selected to be used to determine the performance characteristic forthat discipline. However, the choice of four descriptor measurements ismerely exemplary; fewer or more descriptor measurements may be selected.

In addition, for each riding discipline of interest, an optimalcombination of the selected facial descriptor measurements is determinedfor that riding discipline. In some embodiments, the selected facialdescriptor measurements may be combined linearly or non-linearly. Inother embodiments, functions of the selected facial descriptormeasurements may be combined linearly or non-linearly. In the exemplaryequestrian embodiment described above, the z-scores of the facialdescriptor measurements are combined linearly. The z-score for aparticular facial descriptor measurement of an individual horse may becalculated using the mean and standard deviation of that facialdescriptor measurement for all horses in the same library. Moregenerally, the linear combination coefficients may be determined byapplying multiple regression statistical techniques to the four selectedfacial predictor measurements for all horses in the sample library, allhorses in the sample library that participate in a particulardiscipline, or a subset of horses from the sample library thatparticipate in a particular discipline. The selected subset andcombination of facial descriptor measurements are used to calculate acharacteristic, also called a “performance model,” indicative of theperformance of horses of the same breed in the particular equestrianevent. FIG. 19 shows an exemplary performance model for the WW eventcomprising a linear combination of the z-scores of facial descriptormeasurements AR03_Jowl Protuberance Proportion, AR04_Full Angle Face,AR09_Degree of Ear Flare, and AR11_Eye Size Proportion. This performancemodel for the particular event, e.g., WW, can be applied to other horsesoutside of the sample library to predict the performance in that eventbased upon the selected facial descriptor measurements.

While embodiments have been described above in relation to categorizingand predicting the performance of horses in equestrian show events,embodiments also may be used to categorize and determine equestrianperformance in racing events. This application is very important for avariety of reasons. First, due to the large investment in time andresources required to train a race horse, it is desirable to minimizethe risk of investment by careful choice of which horses to, train forracing. Second, once trained for racing, these horses are poorly suitedto other non racing activities (e.g., dressage, show jumping, pleasureriding etc.), and require a significant investment of time and skill toretrain to a new use off the track. Horses that are trained buteventually found to be unsuitable for racing often are euthanized orsold for slaughter, which could be avoided by more careful preliminaryselection of training candidates.

The methods described above with reference to FIGS. 15 and 16 may beapplied to racing horses in several different ways. First, as athreshold test, embodiments may determine a particular horse'ssuitability for racing. By analyzing features of the facial region, suchas the ones described above, a potential buyer of a yearling raceprospect can determine whether the cognitive and/or behavioralsuitability of that horse for a racing justifies the purchase price andthe necessary follow-on investment in training. Second, for those horsesthat are determined to be suitable for racing, embodiments also maydetermine which of the racing formats (i.e., distances) is most suitedto that horse. For instance, embodiments may be used to determinewhether the horse is best suited for sprints, middle-distance races, orlonger-distance races such as a mile or more. Since each type of evententails a different but expensive training regimen, it is imperative todirect the racing-capable horse to the most suitable event. By ensuringthat horses are placed only into races of distances which they arecognitively suited to handle, such information would allow owners toavoid placing unnecessary strain on their racing prospects that mayultimately lead to costly injuries and even breakdowns. Third,embodiments also may be applied to estimate the performance of aparticular horse in a particular event. All three of these applicationsmay be used individually or in combination.

In some embodiments, the facial analysis methods described above withreference to FIGS. 15 and 16 may be applied to each horse in the fieldof a particular race to predict the outcome of the race based on mentalor other suitability of the horses to particular racing conditions orsituations. For example, in maiden horses races, where natural speed ofthe individual horses is not so great a factor as their lack of racingexperience, the disclosed facial analysis methods may be used to predictthe horse most likely to win based on ability to adapt to the unfamiliarstress and distractions of a race day environment. The disclosed facialanalysis methods may also be used to identify horses likely to placehighly in races due to an innate “will to win”. It is a widely acceptedidea within the horse racing industry that thoroughbred racehorses runfor different for different reasons. Most run simply because they aretold, some run out of the pure joy of doing what they were bred to do,but true champions run because they come to understand the meaning ofvictory and thirst for the praise and attention of the winner's circle.In the final furlongs of long-distance, classic-format races, where allhorses suffer from the exhaustion of considerable lactic acid build up,the courage to rally back and claim the win is especially important indetermining the outcome of the race. In the same manner, the disclosedfacial analysis methods may be used to predict horses unlikely to placehighly in races due to an innate unwillingness to run at the front of orbeyond the pack due either to an inherent lack of confidence, submissivenature, or general inability to overcome herd mentality in the simulatedfight-or-flight scenario of a race environment.

Embodiments of the disclosed facial analysis methods may also be used topredict the performance of a racehorse and their subsequent placing in aspecific race based their response to variable racing conditions. Somehorses become nervous and tire quickly in high-traffic races withconsiderable bumping, and would therefore likely run poorly in large orclosely matched fields. Similarly, some especially sensitive horsesloose heart quickly when running at the back of a large pack where dirtfrom the other horses hooves is kicked up into their face, indicatingthat these horses will likely not place well in races with large fieldswhere their chances of breaking at the front is greatly reduced or undermuddy track conditions. When the innate responsiveness of an individualhorse to such racing conditions—as predicted by the disclosed facialanalysis methods—is taken into consideration with the their previousperformances under standard racing conditions (i.e., racing history) andthe predicted performances of the other horses in the field, themovements of horses within the field during the race and their finalplacing can be predicted by employing the disclosed facial analysismethods. Thus, the methods can be used to predict the performance of anindividual horse in a particular race. Various conditions or factors canbe accounted for by the method, or simply the distance of the race maybe used.

While the methods to predict the outcome of a race are described withrespect to horses, the same method may be deployed with other animalsthat compete or race, for example, dogs or camels.

In other embodiments, the methods described above with reference toFIGS. 15 and 16 may be used to predict non-performance-relatedcharacteristics for a particular breed of horse such as temperament,human interaction, cognitive ability, suitability for a particular task,load-bearing ability, pain tolerance, and/or desirability for breeding.For example, embodiments may determine whether a horse is suitable foruse in a therapeutic riding program for children and/or adults withdisabilities. In the same manner, embodiments may be applied to aid inmatching a particular horse with a particular human disability. Forexample, embodiments may be used to predict that a particular horse isbest suited for persons with cognitive disabilities and that anotherhorse is best suited for persons with physical (non-cognitive)disabilities.

By way of example, embodiments may predict from facial features whethera horse will be cognitively suited to a challenging career as a therapyhorse. Most therapeutic riding (also known as “hippotherapy”) programsare characterized by limited resources and volunteer experience thatoften severely restrict the amount of training that can be invested intonew therapy horses. For a horse to be successful in such a program, theymust have an innate tolerance for the challenging circumstances such asthe often colorful and often loud training tools and the discomfort ofthe claustrophobic and cumbersome riding apparatuses standardly used intherapeutic riding sessions. Such horses must also be incredibly patientand tolerant of the fast, unpredictable, and at times even painfulmovements of their riders both in the saddle and on the ground.

By using the method described above with reference to FIG. 15, a facialanalysis model capable of accurately assessing whether an individualhorse innately possesses such characteristics may be developed viastatistical analysis of common facial structures readily identifiedwithin populations of well suited working therapy mounts. By applyingsuch a model as described above with reference to FIG. 16, therapeuticriding programs can predict which horses would be suitable for workingunder these challenging conditions, drastically reducing the time andresources that they must invest selecting new therapy mounts.

In other embodiments, a facial analysis model can be developed andapplied, as described above with reference to FIGS. 15 and 16, to matchriders of varying physical and cognitive disabilities to the mostappropriate mount. As experience has shown, many therapy horses provebest suited to a specific type of rider. For example, larger and/orolder persons suffering from physical disabilities that limit theirmotor control on the horse, or which can lead to uncontrolled cramping,often work best with calmer and somewhat aloof mounts that are capableof ignoring the discomfort of their rider's limitations. Alternativelyyounger and/or smaller persons, or those suffering from cognitivedisabilities, often benefit from more sensitive and engaged horses thatare able to readily pick up on their fears and respond appropriately tocomfort them and build confidence. A facial analysis model capable ofaccurately predicting such characteristics, as sensitivity andengagement that readily distinguish between horses best suited tophysically or cognitively disabled riders, thereby allowing therapeuticriding programs to more easily match new clients to the most appropriatehorse and avoid the strain of poor pairings felt by both riders andhorses.

By way of example, facial images of a statistically significant numberof working therapy horses that have proven well suited for riders withcognitive disabilities are analyzed to determine desired characteristicphysical features, as described above with reference to FIG. 15. Thisestablishes a model for predicting a candidate horse's suitability forthis particular therapeutic riding task. Subsequently, new candidatehorses may be measured and their suitability for use as a therapeutichorse for riders with cognitive disabilities may be predicted using themethod described above with reference to FIG. 16. Investment intherapeutic horse training can be directed to those horses that arepredicted to be suitable in this manner.

Embodiments may also be used as a preventative measure in horses againstthe development maintenance-based vices. The competitive lifestyles ofhigh-performance show and racehorses are typified by high-concentratediets, extended periods of stall confinement, and excessive stress. Bynature, horses are adapted both mentally and physiologically to highlymobile lifestyles consisting primarily of long periods of time obtainingand digesting high-fiber, forage-based diets. When subjected to theunnatural conditions such as 24-hour stall confinement and limitedactivities that occupy their time, many horses develop undesirablebehaviors such as cribbing, weaving, and stall walking. These behaviorsultimately may be destructive both to facilities and to the horse'shealth. Some horses are by nature at a higher risk of developing thesevices as a function of their innate personalities. Once developed, theseundesirable behaviors are difficult to change. The methods describedabove with reference to FIGS. 15 and 16 could be used to predict horseslikely to exhibit these behaviors, so that preventative maintenancesolutions could be employed before these behavioral patterns becomeestablished.

Since the domestication phenotype is common across many species ofanimals, embodiments, including those described above, are applicable toa broad range of animals, both mammals and non-mammals, such as donkeys,cattle, oxen, llamas, sheep, goats, turkey, geese, dogs, foxes, cats,ferrets, camels, geese, chickens, pigs, fish, etc. For example,embodiments may be used to predict a characteristic, such as motheringability, of a sow based upon the anatomical structures of the sow'sface. One suitable metric of mothering ability is the expected number ofmash deaths that a sow would incur per litter of piglets. Initially, oneor more digital images of a sow's face (e.g., front, profile, and/or topviews) are obtained and stored in a memory operably connected to adigital computer. Additional data may be associated with the images,such as mash death statistics, which may be obtained from farrowingrecords regularly kept for hog farms. This step may be repeated asnecessary based upon the number of sows in a group of interest.

Next, for each sow of interest, a plurality of reference points areadded to the one or more stored images in a manner such as describedabove with reference FIGS. 15 and 16. Using these reference points, aplurality of facial measurements are determined from the one or morestored digital images for each sow of interest. The plurality of facialmeasurements may be—but are not necessarily—similar to the ones forhorses discussed above with reference to FIGS. 1 through 13.Relationships between each of the facial descriptor measurements and theadditional data of the sample library are then determined. Using theserelationships, facial descriptor measurements are selected and anoptimal combination of the selected facial descriptor measurements isdetermined, based on being most predictive of the actual mash deathstatistics of the sows in the sample library. Statistical techniquessuch as those described above with reference to FIGS. 18-21 may be usedin the subset selection, or any other appropriate techniques known topersons skilled in the art may be used.

The selected facial descriptor measurements and the optimal combinationthen can be applied to determine the mothering ability characteristic ofa particular sow based on the facial descriptor measurements for thatanimal, in the manner described above with reference to FIG. 16. Inother words, the combination of measurements selected based on thesample library may be applied to sows of interest outside the samplelibrary to predict the mothering ability of those animals. As thisembodiment is applied to new sows, the sample library can be augmentedby adding the images, additional data, and facial descriptormeasurements for these animals. Selection of facial descriptormeasurements and their optimal combination then can be carried out usingthe augmented sample library, in the same manner as described above andillustrated in FIG. 16.

In this manner, using facial descriptor measurements, an embodiment canbe used to predict animal characteristics such as number or percentageof mash deaths per litter that a sow would be naturally inclined toincur. Furthermore, other embodiments utilize these predictedcharacteristics to determine management and/or maintenance strategies touse for individual high-risk sows. These strategies may includeincreasing supervision, using larger pens, moving piglets off a sow toreduce litter size, etc. Selection of one or more of these strategiesmay be based on a sow's predicted mash death rate and, optionally, oneor more environmental factors such as farrowing crate dimensions, etc.and/or one or more other factors such as the sow's age, size, weight,and average litter size.

In some embodiments, other non-facial measurements or metrics thatrelate to influences on the behavioral development of an animal duringthe key imprinting windows of early life may also be used in combinationwith the facial measurements in the analysis. For example, and withoutlimitation, such non-facial measurements or metrics may include birthweight, sow weaning weight, litter weight percentile, gender compositionof litter, age, litter number, nursery pen weight, percentile, finishingpen weight percentile, gestation weight percentile, and leg length. Inother embodiments, the predicted mothering behavior may be used toselect gilts as replacements for a maintenance rotation, allowing swinefarmers to avoid the financial loss of feeding gilts to farrowing ageonly to have them prove unsuitable as mothers.

Other embodiments may be used to determine maintenance and/or managementstrategies for one or more animals, as illustrated in FIG. 17. In step1700, one or more optimal combinations of facial descriptor measurementsare selected for predicting one or more characteristics of interest fora particular type of animal. Step 1700 may be carried out according tothe method described above with reference FIG. 15, or in other wayswithin the scope of the present embodiment. The one or morecharacteristics of interest preferably are related to the maintenanceand/or management strategies under consideration. As discussed above,step 1700 may include generation of a sample library for a particulartype of animal comprising digital images, additional data, and aplurality of facial descriptor measurements. The facial measurements maybe—but are not necessarily—similar to the ones for horses discussedabove with reference to FIGS. 1 through 13. Non-facial physicalmeasurements may also be used with the method.

In step 1702, digital images and additional data for a particular animalof interest are obtained and stored, in the same manner as describedabove with reference to the sample library (e.g., step 1500 of FIG. 15).In step 1704, reference points consistent with those added to the imagesin the sample library are added to the images of the animal of interest.In step 1706, facial descriptor measurements consistent with those inthe sample library are calculated for the animal of interest. In step1708, the one or more optimal combinations determined in step 1700 areapplied to the facial descriptor measurements of the individual animalto predict the characteristic of interest, in a manner such as describedabove with reference to FIG. 16. Steps 1702 through 1708 are repeatedfor each individual animal included in the maintenance and/or managementstrategy. In step 1712, the predicted characteristics of interest forthe individual animals are used to predict one or more characteristicsfor the group of animals. Finally, in step 1714, the predicted groupcharacteristics and/or predicted individual characteristics are used todetermine a maintenance and/or management strategy for the group ofanimals. Other factors and information may be used in either or both ofsteps 1712 and 1714, including factors related to individual animals,factors related to the group of animals, and/or factors related to theenvironment of the animals.

By way of example, the embodiment illustrated by FIG. 17 may be used topredict aggression-related characteristics of individual sows and towhat extent certain combinations of sows will cohabitate in a gestationpen without harming each other. Step 1700 may include selecting facialdescriptor measurements and an optimal combination of the selectedfacial descriptor measurements that is most predictive ofcharacteristics related to fighting in a gestation pen. Examplecharacteristics include food aggression, territoriality, dominance innovel environments, etc. Statistical techniques such as those describedabove with reference to FIGS. 18-21 may be used to select the facialdescriptor measurements and their optimal combination(s). Otherappropriate statistical techniques known to persons skilled in the artalso may be used. To the extent that characteristics related to fightingin a gestation are independent or not fully correlated with each other,multiple subsets of facial descriptor measurements and/or multipleoptimal combinations may be selected to predict the respectivecharacteristics.

The selected facial descriptor measurements and optimal combination thencan be applied to predict or estimate an individual sow's naturalaggression/dominance level in each relevant personality characteristic.Information other than the facial descriptor measurements may beincorporated into this prediction or estimate, including the masculinityratio of the sow's lactation litter, which indicates the level oftestosterone that the sow was exposed to during conception, and weightrank of the sow within its lactation litter.

Furthermore, in some embodiments, this prediction or estimate of a sow'saggression-related characteristics may be used to predict the outcome ofthat sow's social interactions with one or more other sows in agestation pen, or an overall level of aggressive behavior in a gestationpen. For example, observations about the number of gestation pen fightsand the predicted aggression-related characteristics for sows ofinterest may be used to determine a weighted model for gestation penaggression that is independent of the composition of animals within apen. Alternately, these observations and predictions ofaggression-related characteristics may be used to predict or estimatethe number of fights a particular sow would engage in a gestation penwith a particular composition of animals.

More specifically, observations and predictions of aggression-relatedcharacteristics may be used to segment the sows of interest intomultiple aggression levels or categories, such as I (“DominantAggressive”) II (“Subordinate Defensive”), and III (“SubmissiveEvasive”). After sows of interest are categorized accordingly, a groupcharacteristic such as the frequency of social aggression can becomputed based on the number of animals of each category in the pen. Agroup characteristic such as the frequency of social aggression amongsows cohabitating in a gestation pen also can be predicted by firstaveraging each of the selected facial descriptor measurements across allindividuals comprising the group and then computing an optimized linearcombination of the group averages.

By further example, a group characteristic such as aggression level canbe predicted according to the method illustrated in FIG. 17 as follows.First, the optimal combination of facial descriptor measurementsdetermined in step 1700 can be utilized according to steps 1702 through1710 to predict the social aggressiveness of individual sows in thegroup. Next, the set of predicted social aggression levels can becombined optimally to form a predictor of the level of social aggressionwithin the group. In other words, the predicted social aggression levelfor the ith sow in the group can be expressed as SA₁=a₁·F₁+a₂·F₂+ . . .+a_(M)·F_(M), where F₁ . . . F_(M) are the selected facial descriptormeasurements and a₁ . . . a_(M) are the optimal linear combiningcoefficients. Likewise, if the group comprises N individual sows, thepredicted group social aggression can be expressed as GSA=b₁·SA₁+b₂·SA₂+. . . +b_(N)·SA_(N), where b₁ . . . b_(N) are the optimal linearcombining coefficients for the group. Furthermore, in the combined groupmodel, the individual sows may be ordered in a variety of ways. Forexample, the predicted individual aggression levels SA₁ . . . SA_(N) maybe rearranged in descending numerical order SA′₁ . . . SA′_(N), withSA′₁ corresponding to the most aggressive sow. In such case, eachoptimal coefficient b_(i) may be selected, at least in part, based onthe position within the group. This would allow, for example,coefficients b₁ to be selected to emphasize the characteristics of themost aggressive and/or submissive animals in the group.

Group dynamics and group characteristics such as aggression level amonga group of sows in a gestation pen can be predicted in various otherways known to persons skilled in the art. For example, methods may beemployed that are analogous to the Hartree-Fock equation, which iscommonly used to predict the behavior of the electrons in an atom.Alternately, a computational method based on a NETLOGO model can beused. An advantage of this approach is that factors other than theaggression-related behavior traits predicted from the facial descriptormeasurements can be incorporated. Such factors may include the estimatedactivity level and the resulting number of interaction between animalsin a pen, as well as environmental factors such as crowding.

While the group dynamics and group characteristics have been describedwith references to pigs, the same or similar methods and systems can beapplied to various other animals. In some embodiments, the disclosedmethods may be used to determine appropriate pasture assignments onhorse boarding facilities, where pasture injuries can at best beaesthetically unpleasing in show horses and at worst permanentlycrippling and costly. In other embodiments, the disclosed methods may beused to identify which dogs are behaviorally suited to group-pen housingin kennel situations, and those that need individual enclosures tominimize fighting.

Persons of ordinary skill in the art would understand that any of thesecomputational methods may be embodied in various combinations ofhardware and software. For instance, the computations may be carried outby a specialized or general-purpose digital computer, such as a laptop,desktop, tablet, smartphone, workstation, etc. Moreover, this hardwaremay be programmed to carry out such computations in various ways, suchas by programs written in human-readable languages such as C, C++, etc.and compiled into machine-readable code for execution. Alternately, themethods may be expressed in the particular language of a specializedcomputational software package, such as Matlab, which are furtherinterpreted and/or compiled into machine-readable code. In the samemanner, computerized selections may be carried out by accessing one ormore electronically stored database(s) comprising information about theanimal of interest; other animals of the same type, species, or breed;environmental factors; and other relevant information.

For example, FIG. 22 shows various hardware and software embodiments ofa system according to embodiments of the present disclosure. Varioushardware devices—such as digital camera 2204, smartphone 2206, cellularphone 2208, tablet computer 2210, and laptop computer 2212—may be usedto capture a digital image of an animal of interest, such as theexemplary horse 2202. The image-capturing device may then store theimage in a memory operably connected to a digital computer. This may becarried out in various ways as illustrated in FIG. 22. For example, theimage capturing device may transmit the image through network 2216 viawireless access point 2214 to digital computer 2220, which may be adesktop computer, server, workstation, or the like. Moreover, wirelessaccess point 2214 may be a cellular base station, wireless LAN accesspoint, Bluetooth access point, or any other wireless connection known topersons of ordinary skill in the art. Likewise, network 2216 may be alocal- or wide-area, public or private network, or any combinationthereof, including an intranet and/or the Internet.

In other embodiments, the image capturing device transfers the captureddigital image to digital computer 2220 through a wired connection 2222,such as a Universal Serial Bus (USB) connection. In yet otherembodiments, the captured image(s) may be transferred by removing amemory card from the image capturing device and inserting it into memorycard reader 2224 of digital computer 2220, which may copy the capturedimages to other memory accessible by or operably connected to digitalcomputer 2220. Also within the spirit and scope of the presentdisclosure, the image capturing device may transfer the image, viamethods described above or otherwise well known in the art, to devicesother than digital computer 2220, such as tablet computer 2210. In suchembodiments, further processing according to the methods describe abovewill occur, for example, in tablet computer 2210 rather than in digitalcomputer 2220. Similarly, the image capturing device may transfer theimage to network storage unit 2226 that is accessible via network 2216,e.g., cloud storage. Network storage unit 2226 may be configured to beaccessible by some or all of the other devices shown in FIG. 22.

In other embodiments, further processing according to the methodsdescribed above also may take place in the image capturing deviceitself. For example, tablet computer 2210 may be used to capture imagesof animals of interest, store the images in memory accessible oroperably connected to it (including, for example, network storage unit2226), and then execute one or more software applications embodying oneor more methods described above. Specific measurements or processed datafrom the image capturing device may also be communicated to a centralcomputer or central location.

Although embodiments described above are related to predictingcharacteristics of animals, other embodiments within the scope of thepresent disclosure may be used to predict certain characteristics ofhumans via methods employing facial descriptor measurements. Duringprogressive stages of human embryonic growth, development of the brainand face remains intimately connected through both genetic signaling andbiomechanical/biochemical mechanisms. The face develops from populationsof cells originating from the early neural crest, with cells from theneural tube gradually shifting to form the prominences of the face.Differentiation of these early cells is closely regulated throughintricate genetic signaling mechanisms, with the brain essentiallyserving as the platform on which the face grows. As these two structurescontinue to grow and develop during the later embryonic stages, theirphenotypes remain closely linked as complex genetic hierarchies regulatepatterns of cross talk between molecules, cells, and tissues.

The close relationship between the functional development of the brainand structures of the face has been clearly documented for a number ofdevelopmental disorders. Among the most well known of these disorders isFetal Alcohol Syndrome (FAS), which is the direct result of exposure ofthe fetus to alcohol during pregnancy. FAS has been shown to result inboth an easily identifiable phenotype (i.e., collection of minor facialabnormalities such as small eyes, smooth philtrum, thin upper lip) anddevelopmental damage to the central nervous system that is oftenpermanent (e.g., speech delays, learning disabilities, poor reasoningskills, poor memory, attention deficit disorders, and low IQ). FIGS. 23through 30 show a set of fifteen (15) human facial descriptormeasurements that can be used to identify the phenotype associated withFAS. For example, FIGS. 23A through 23F show various facial descriptormeasurements related to the eye, while FIGS. 28 and 30 show variousfacial descriptor measurements related to the philtrum. Nevertheless,this set of human facial descriptor measurements is merely exemplary,and the person of ordinary skill will recognize that fewer than theentire set may be used to predict FAS. Furthermore, this set of humanfacial descriptor measurements is not exhaustive and others may beincorporated, such as with the set of fifty-six equine facial descriptormeasurements shown in FIGS. 1 through 13. Is some embodiments, two,three, or more of the facial descriptor measurements are used incombination to predict a trait, characteristic, or syndrome such as FAS.

While examples are shown primarily with facial measurements, other headmeasurements and physical measurements may be used alone, without facialmeasurements, or in combination with facial measurements. For example,measurements of the head or crown may be used in conjunction with facialfeatures to predict syndromes or traits.

By way of further example, Down syndrome is another prenataldevelopmental disorder causing mental/social delays that yields aneasily identifiable phenotype including a host of distinctive facialfeatures such as small chin and/or mouth, round face, and roundedalmond-shaped eyes. Recent studies have even been able to identifymeasurable trends in facial features that distinguish between childrendiagnosed with Autism Spectrum Disorders (ASD) and those of typicaldevelopment. The facial descriptor measurements shown in FIGS. 23through 30 may also be used to identify the phenotype associated withDown syndrome. However, as with FAS described above, this set of facialdescriptor measurements is merely exemplary, and may be reduced oraugmented for predicting Down syndrome or other human functionaldevelopment disorders within the spirit and scope of the presentdisclosure.

Given these established relationships between human facial structuresand cognitive development, any of the computationally inexpensive,two-dimensional, locally-normalized facial evaluation systems describedprovides a non-invasive analysis tool for use in multiple clinicalapplications. For example, embodiments of the facial analysis methodsand systems disclosed herein will diagnose children with specificcognitive development disorders based on the degree of divergencebetween their facial features and those of the overall typicalpopulation with respect to the phenotype for a disorder. Such adiagnosis tool is faster and less invasive than the standard cognitivetesting procedures currently employed, and may allow for earlierdiagnosis and interventions. More computationally expensive embodimentsor variations may also be used for diagnosis.

In the same manner, embodiments of the facial analysis methods andsystems disclosed herein can be used for predicting cognitive disordersthat vary in degree of severity (e.g., FAS and Down Syndrome) or whichpresent as a spectrum of disorders (e.g., ASD,). Such embodimentsutilize metrics such as continuously-valued, facial descriptormeasurements to produce multivariate models capable of predictingseverity or types of symptoms displayed. For example, a facial analysismodel derived using methods described herein could be used tostatistically predict a child's score on the Childhood Autism RatingScale (CARS). CARS is a diagnostic standard that uses behavioralobservations and testing to differentiate between Autism and other typesdevelopmental disorders and to quantify the severity of the disorderalong a scale.

By way of further example, a facial analysis model derived using methodsdescribed herein can be used to predict which category, class, and/orsubclass of a particular cognitive disorder that a particular individualfalls within. For example, a model based on a plurality of facialmeasurements will predict whether a particular individual is betterdiagnosed with classic autism or with Asperger's syndrome. Thispredicted classification could be used by itself, or in conjunction withother qualitative and/or quantitative diagnostic methods, to develop ofthe most appropriate and effective plan for early therapeuticintervention and treatment.

Embodiments of the facial analysis methods and systems disclosed hereinalso can be used for predicting or inferring certain characteristics ofhuman individuals that do not have cognitive or developmental disorders.For example, a model based on a plurality of facial measurements may beused in the manner described above with reference to FIGS. 15 and 16 topredict a particular individual's innate personality such as aggressionand competitiveness. One or more of the facial descriptor measurementsshown in FIGS. 23 through 30 may also be used for this purpose. Asdescribed above, however, this set of facial descriptor measurements ismerely exemplary and may be reduced or augmented as necessary within thespirit and scope of the present disclosure. Furthermore, multivariateanalysis of a plurality of facial measurements statistically normalizedagainst like measurements of a standard population of individuals can beused to predict other variable aspects of innate personality such asaggression, dominance, conscientiousness, compassion, extroversion, IQ,etc. Insight into such personality traits of an individual could be usedas an inference tool to predict various aspects of behavior andperformance such as learning styles, athletic performance, businessdecisions, etc.

For example, facial and other physical measurements of successfulleft-handed baseball pitchers can be made and compared with measurementsof a standard population. Digital images and reference points may beused to ease the burden of the measurement process. Ratios, angles, ornominal measurements may be used instead of or in addition to actualmeasurements. A number of metrics may then be analyzed to find thosemetrics which show the greatest correlation or significance indetermining statistically whether a person is more likely than anotherto be a successful left-handed pitcher. Once the more significantmetrics are identified, the system may simply rely on those metrics topredict a person's strength or likelihood to succeed as a left-handedpitcher. A baseball scout may use the system as a tool to assist inchoosing one left-handed pitching prospect over another by scoring bothcandidates. Those skilled in the art will recognize numerousapplications of the methods and techniques described throughout thespecification to humans.

In other embodiments of the methods and systems disclosed herein, facialanalysis of both animals and humans can be used to pair a person (i.e.,a human being) with a particular animal based on the degree that theirpersonalities complement each other. For example, a model based on aplurality of facial measurements could be developed and applied in themanner described above with reference to FIGS. 15 and 16 to predict thepersonality characteristics of a variety of horses that are availablefor clients of a therapeutic riding program. When a new client enrollswith the program, facial analysis is also used to predict the client'srelevant characteristics, e.g., self-awareness. With this information,the program could then find the horse with facial features thatcorrespond to characteristics that are necessary to accommodate thisclient, e.g., high pain tolerance levels and high responsiveness neededsince the client is likely to lose their balance frequently. By the sametoken, if a facial analysis program indicated that a new autistic clientis easily frustrated and likely to act out, then it could pair thatclient to a horse with facial features that indicate high tolerancelevels that allow them to deal with the loud noises and fast movements,as well as some degree of stubbornness to stay on task even when theclient is not doing what they are supposed to be doing.

Additionally, these embodiments can be used in a wide variety ofapplications to identify or predict the optimum or most appropriatecombination of a human and animal. For example, for a particular type ofevent or race, horses may be paired with their most appropriateriders—or vice versa—thereby optimizing the competitive performance ofthe pair. Moreover, in training environments, horses may be matched withthe most appropriate trainer to improve communication and cohesivenessand increase learning potential. By the same token, young riders may bematched to the horse that will best accommodate their needs. Similarly,embodiments may be used in non-equine applications such as to matchseeing-eye dogs to the best owners or drug/bomb dogs to the best suitedhandlers based on the unique personalities of animal and human.

A method according to this embodiment is shown in FIG. 31. In thismethod, it is assumed that facial descriptor measurements werepreviously computed for the set of animals of interest, e.g., the horsesavailable in a therapeutic riding program, and matched to a single humanof interest. However, a person of ordinary skill will easily recognizethat the facial descriptor measurements could be pre-computed for a setof humans, e.g., clients at a therapeutic riding program, and matched toan animal of interest, e.g., a new horse. In block 3100, digital imagesand additional data is obtained and stored for a human, such as in themanner described previously with reference to the sample library (i.e.,block 1500 of FIG. 15). In block 3102, reference points consistent withthose of the images in the sample library are added to the images of thehuman. In block 3104, facial descriptor measurements are calculated forthe human. For example, the reference points and facial descriptormeasurements may be those shown in FIGS. 23 through 30, augmented byadditional facial descriptor measurements as necessary.

In block 3106, a combination of one or more of the facial descriptormeasurements of the human and one or more of the facial descriptormeasurements of a particular animal of interest (e.g., a horse) are usedto predict the relevant characteristics of that particular human-animalcombination. Block 3108 determines whether or not to repeat block 3106for another animal of interest. After determining the relevantcharacteristics for each particular human-animal combination, in block3110 the animal providing the best match to the human is selected basedon the relevant characteristics of human-animal combination. Persons ofordinary skill will recognize that FIG. 31 may be adapted to otherembodiments by combining facial descriptor measurements for anindividual animal with previously computed facial descriptormeasurements for a plurality of humans to select the best combination.

To find suitable pairings of animals and humans, any of the embodimentsdescribed above may be used to determine characteristics of the animaland any of the embodiments described may be used for determiningcharacteristics of humans. Other physical metrics in addition to facialfeatures may be used. The results of the animal characteristics andhuman characteristics are analyzed for suitability, best fit or a match.

Moreover, the terms and descriptions used herein are set forth by way ofillustration only and are not meant as limitations. Those skilled in theart will recognize that many variations are possible within the spiritand scope of the disclosure as defined in the following claims, andtheir equivalents, in which all terms are to be understood in theirbroadest possible sense unless otherwise indicated.

What is claimed is:
 1. A computerized method for predicting one or morecharacteristics of a human based on one or more digital imagesrepresenting the human, comprising: receiving the one or more digitalimages representing the human, wherein a portion of at least one of thedigital images representing the human's head is annotated with aplurality of reference points; associating at least one other data valueabout the human with the one or more digital images representing thehuman; computing, with a digital computer: a plurality of scale-freemetrics comprising at least two of the following: a length metric, anarea metric, and curvature measures using relative locations of apredetermined subset of the reference points and descriptive points,wherein the descriptive points are based on the relative locations ofthe reference points; a combined metric based on a predeterminedfunction of the plurality of scale-free metrics; selecting, usingdimension reduction techniques, a combination of the plurality ofscale-free metrics useable for predicting a characteristic of the humanbased on the combined metric and data about a group of subjects; andproviding the predicted characteristic to an output device.
 2. Thecomputerized method of claim 1, further comprising: storing the one ormore digital images in a memory operably connected to the digitalcomputer; and annotating the portion of at least one of the digitalimages representing the human's head with the plurality of referencepoints.
 3. The computerized method of claim 1, wherein the predeterminedfunction is determined based on data relating to a plurality of otherhumans.
 4. The computerized method of claim 1, wherein the predictedcharacteristic relates to a developmental disorder.
 5. The computerizedmethod of claim 1 wherein the predicted characteristic comprises arating on a childhood autism rating scale.
 6. The computerized method ofclaim 1, wherein the predicted characteristic relates to athleticperformance.
 7. The computerized method of claim 1 wherein one or moreof the plurality of scale-free metrics are computed as z-scores.
 8. Thecomputerized method of claim 1, wherein the predicted characteristicrelates to one or more of a) type of sporting event most suitable forthe human, and b) the expected performance of the human in a particularposition on a sports team.
 9. The computerized method of claim 1,wherein the human is physically or mentally disabled.
 10. Thecomputerized method of claim 1, wherein the predicted characteristic isfurther based on at least one of: a) additional information related tothe human; and b) information related to training the human hascompleted; and c) information relating to the human's environment orsituation.
 11. The computerized method of claim 1, wherein at least oneof the at least two measures of the plurality of scale-free metricscomprises reference points related to the human's eyes.
 12. Thecomputerized method of claim 1, wherein the plurality of scale-freemetrics comprises two or more of the following: Eye Depth Proportion,Eye Height Proportion, Eye Length-to-Height Proportion, Mid-Face WidthProportion, Upper Eyelid Proportion, Eye Roundness Proportion, PhiltrumDefinition Proportion, Upper Lip Thickness Proportion, Lower LipThickness Proportion, and Philtrum Length Proportion.
 13. Thecomputerized method of claim 1, wherein providing the predictedcharacteristic to an output device comprises displaying the predictedcharacteristic.
 14. A computerized system for predicting one or morecharacteristics of a human based on one or more digital imagesrepresenting the human, comprising: at least one memory storingcomputer-executable instructions and the one or more digital imagesrepresenting the human; a processor in communication with the at leastone memory, executing the stored instructions, causing the processor to:receive the one or more digital images representing the human, wherein aportion of at least one of the digital images representing the human'shead is annotated with a plurality of reference points; associate atleast one other data value about the human with the one or more digitalimages representing the human; compute a plurality of scale-free metricscomprising at least two of the following: a length metric, an areametric, and curvature measures using relative locations of apredetermined subset of the reference points and descriptive points,wherein the descriptive points are based on the relative locations ofthe reference points; compute a combined metric based on a predeterminedfunction of the plurality of scale-free metrics; select, using dimensionreduction techniques, a combination of the plurality of scale-freemetrics useable for predicting a characteristic of the human based onthe combined metric and data about a group of humans; and provide thepredicted characteristic to an output device.
 15. The computerizedsystem of claim 14, wherein the predicted characteristic relates to adevelopmental disorder.
 16. The computerized system of claim 14, whereinthe predicted characteristic comprises a rating on a childhood autismrating scale.
 17. The computerized system of claim 14, wherein thepredicted characteristic relates to athletic performance.
 18. Anon-transitory, computer-readable medium comprising a set ofinstructions that, when executed by a computing device, causes thecomputing device to: receive one or more digital images representing ahuman, wherein a portion of at least one of the digital imagesrepresenting the human's head is annotated with a plurality of referencepoints; associate at least one other data value about the human with theone or more digital images representing the human; compute a pluralityof scale-free metrics comprising at least two of the following: a lengthmetric, an area metric, and curvature measures using relative locationsof a predetermined subset of the reference points and descriptivepoints, wherein the descriptive points are based on the relativelocations of the reference points; compute a combined metric based on apredetermined function of the plurality of scale-free metrics; select,using dimension reduction techniques, a combination of the plurality ofscale-free metrics useable for predicting a characteristic of the humanbased on the combined metric and data about a group of humans; andprovide the predicted characteristic to an output device.
 19. Thenon-transitory, computer-readable medium of claim 18, wherein thepredicted characteristic relates to a developmental disorder.
 20. Thenon-transitory, computer-readable medium of claim 18, wherein thepredicted characteristic comprises a rating on a childhood autism ratingscale.
 21. The non-transitory, computer-readable medium of claim 18,wherein the predicted characteristic relates to athletic performance.